{ "aboutHtml": "

Nano Banana Pro 是 Google 以 Gemini 3 Pro 為基礎的圖像生成及編輯模型。它接收文字與圖像，回傳圖像或文字。公開設定涵蓋資訊圖表、圖解、產品視覺、分鏡，以及結合多個元素或參考的構圖。

規劃圖像及其可見內容

說明用途、主要主體及元素關係。資訊圖表應提供實際數據及確切標籤，再描述層級。產品場景應指明物件、視角、環境及必須保持可辨識的細節。模型文件記載的文字呈現用途包含較長措辭及多語版面；請檢查圖像中回傳的字母、數字及對齊。

文件記載 Search grounding 可將最新資訊納入視覺任務。介面提供此能力時，應隨需求保留來源事實，並檢查它們如何呈現在最終圖像。視覺上令人信服的圖表或圖解，仍應與預定呈現的資料及標籤比較。

參考與創意編輯

公開代管設定接受最多十四張參考圖像。請為每張指定角色，例如主體、環境或視覺風格，而非不說明彼此關係。模型描述強調最多五個主體的身分保留；應在整體回傳構圖中比較實際主體及重要細節。

編輯時應指出要修改的區域或物件，以及要保留的元素。文件中的創意控制包括局部修改、光線與焦點調整、鏡頭變換及彈性長寬比。檢查所需編輯及應保持可辨識的細節時，請保留來源圖像。

解析度與可用成果

此設定記載 2K 及 4K 輸出。請依預期放置位置選擇視覺比例及輸出規模，再檢查實際回傳尺寸與檔案屬性。圖像進入較大版面時，設計概念與匯出要求應保持連結。

將選定圖像與需求、參考角色及修訂歷史一起保留。以素材實際使用尺寸檢查確切文字、主體連續性及資訊層級。實用交付成果是此模型設定經檢查的視覺素材或附帶文字，可供後續設計或製作。

", "aboutText": "Nano Banana Pro 是 Google 以 Gemini 3 Pro 為基礎的圖像生成及編輯模型。它接收文字與圖像，回傳圖像或文字。公開設定涵蓋資訊圖表、圖解、產品視覺、分鏡，以及結合多個元素或參考的構圖。\n\n### 規劃圖像及其可見內容\n\n說明用途、主要主體及元素關係。資訊圖表應提供實際數據及確切標籤，再描述層級。產品場景應指明物件、視角、環境及必須保持可辨識的細節。模型文件記載的文字呈現用途包含較長措辭及多語版面；請檢查圖像中回傳的字母、數字及對齊。\n\n文件記載 Search grounding 可將最新資訊納入視覺任務。介面提供此能力時，應隨需求保留來源事實，並檢查它們如何呈現在最終圖像。視覺上令人信服的圖表或圖解，仍應與預定呈現的資料及標籤比較。\n\n### 參考與創意編輯\n\n公開代管設定接受最多十四張參考圖像。請為每張指定角色，例如主體、環境或視覺風格，而非不說明彼此關係。模型描述強調最多五個主體的身分保留；應在整體回傳構圖中比較實際主體及重要細節。\n\n編輯時應指出要修改的區域或物件，以及要保留的元素。文件中的創意控制包括局部修改、光線與焦點調整、鏡頭變換及彈性長寬比。檢查所需編輯及應保持可辨識的細節時，請保留來源圖像。\n\n### 解析度與可用成果\n\n此設定記載 2K 及 4K 輸出。請依預期放置位置選擇視覺比例及輸出規模，再檢查實際回傳尺寸與檔案屬性。圖像進入較大版面時，設計概念與匯出要求應保持連結。\n\n將選定圖像與需求、參考角色及修訂歷史一起保留。以素材實際使用尺寸檢查確切文字、主體連續性及資訊層級。實用交付成果是此模型設定經檢查的視覺素材或附帶文字，可供後續設計或製作。", "editorialOverlay": { "copy_fields": { "practical_html": "

實用任務

英文技術範例

Create a stock comparison infographic\n\nCreate a clean landscape infographic for a warehouse noticeboard. Title: "Weekly stock". Show exactly three labelled bars: "Boxes 24", "Bags 12", "Tubes 6". Use a white background, dark text and one blue accent. Keep labels large, align the bars to a common baseline and add no extra numbers or logos.

預期結果

One infographic image with the exact title and three proportional, labelled bars on a common baseline.

檢查結果

Inspect all lettering and confirm the bar values and relative lengths match 24, 12 and 6.

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G

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

Google /google/gemini-3-pro-image-preview

入門

Nano Banana Pro 是 Google 用於圖像生成及編輯的 Gemini 3 Pro Image Preview 模型。文件記載的設定支援多圖構圖、內嵌文字、局部創意修改、Search grounding，以及 2K 或 4K 輸出。

接受的輸入: 文字
結果: 文字

關於模型

此說明介紹模型的能力。Ailoot 可用的輸入、輸出與選項，依所選方法列於下方。

Nano Banana Pro 是 Google 以 Gemini 3 Pro 為基礎的圖像生成及編輯模型。它接收文字與圖像，回傳圖像或文字。公開設定涵蓋資訊圖表、圖解、產品視覺、分鏡，以及結合多個元素或參考的構圖。

規劃圖像及其可見內容

說明用途、主要主體及元素關係。資訊圖表應提供實際數據及確切標籤，再描述層級。產品場景應指明物件、視角、環境及必須保持可辨識的細節。模型文件記載的文字呈現用途包含較長措辭及多語版面；請檢查圖像中回傳的字母、數字及對齊。

文件記載 Search grounding 可將最新資訊納入視覺任務。介面提供此能力時，應隨需求保留來源事實，並檢查它們如何呈現在最終圖像。視覺上令人信服的圖表或圖解，仍應與預定呈現的資料及標籤比較。

參考與創意編輯

公開代管設定接受最多十四張參考圖像。請為每張指定角色，例如主體、環境或視覺風格，而非不說明彼此關係。模型描述強調最多五個主體的身分保留；應在整體回傳構圖中比較實際主體及重要細節。

編輯時應指出要修改的區域或物件，以及要保留的元素。文件中的創意控制包括局部修改、光線與焦點調整、鏡頭變換及彈性長寬比。檢查所需編輯及應保持可辨識的細節時，請保留來源圖像。

解析度與可用成果

此設定記載 2K 及 4K 輸出。請依預期放置位置選擇視覺比例及輸出規模，再檢查實際回傳尺寸與檔案屬性。圖像進入較大版面時，設計概念與匯出要求應保持連結。

將選定圖像與需求、參考角色及修訂歷史一起保留。以素材實際使用尺寸檢查確切文字、主體連續性及資訊層級。實用交付成果是此模型設定經檢查的視覺素材或附帶文字，可供後續設計或製作。

Ailoot 可用 · 輸入與方法

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions

輸入: text
輸出: text
輸出格式: json 或 SSE

欄位	類型	要求	值與限制	預設值
欄位`model`	類型`string`	要求必填	值與限制 google/gemini-3-pro-image-preview	預設值—
欄位`messages`	類型`message[]`	要求必填	值與限制項目數下限: 1	預設值—
欄位`messages[].role`	類型`string`	要求必填	值與限制範例：system, user, assistant, tool, function 條件: 這些角色由原始碼中的相容型別明確表示；確切角色集合仍依模型而定。依模型而定	預設值—
欄位`messages[].content`	類型`string\|content_part[]\|null`	要求選填	值與限制	預設值—
欄位`messages[].tool_calls`	類型`tool_call[]`	要求選填	值與限制	預設值—
欄位`messages[].tool_call_id`	類型`string`	要求選填	值與限制適用條件: role=tool；使用實際回傳的 ID	預設值—
欄位`messages[].name`	類型`string`	要求選填	值與限制	預設值—
欄位`messages[].reasoning_content`	類型`string`	要求選填	值與限制適用條件: 此模型使用推理內容時，將實際回傳的推理內容帶回後續請求	預設值—
欄位`messages[].reasoning_details`	類型`object\|array`	要求選填	值與限制依模型而定	預設值—
欄位`max_tokens`	類型`integer`	要求選填	值與限制最小值: 1 依模型而定	預設值—
欄位`max_completion_tokens`	類型`integer`	要求選填	值與限制最小值: 1 依模型而定	預設值—
欄位`stream`	類型`boolean`	要求選填	值與限制	預設值false
欄位`stream_options.include_usage`	類型`boolean`	要求選填	值與限制依模型而定	預設值—
欄位`temperature`	類型`number`	要求選填	值與限制依模型而定	預設值—
欄位`top_p`	類型`number`	要求選填	值與限制依模型而定	預設值—
欄位`stop`	類型`string\|string[]`	要求選填	值與限制依模型而定	預設值—
欄位`tools`	類型`function_tool[]`	要求選填	值與限制依模型而定	預設值—
欄位`tools[].type`	類型`string`	要求選填	值與限制 function 依模型而定	預設值—
欄位`tools[].function.name`	類型`string`	要求必填	值與限制適用條件: 已提供函式工具	預設值—
欄位`tools[].function.description`	類型`string`	要求選填	值與限制	預設值—
欄位`tools[].function.parameters`	類型`JSON_schema`	要求選填	值與限制	預設值—
欄位`tool_choice`	類型`string\|object`	要求選填	值與限制依模型而定	預設值—
欄位`parallel_tool_calls`	類型`boolean`	要求選填	值與限制依模型而定	預設值—
欄位`response_format`	類型`object`	要求選填	值與限制依模型而定	預設值—
欄位`reasoning_effort`	類型`string`	要求選填	值與限制依模型而定	預設值—
欄位`reasoning`	類型`object`	要求選填	值與限制依模型而定	預設值—
欄位`service_tier`	類型`string`	要求選填	值與限制依模型而定	預設值—
欄位`instructions`	類型`string`	要求選填	值與限制相容性欄位	預設值—
欄位`functions`	類型`function[]`	要求選填	值與限制依模型而定	預設值—
欄位`function_call`	類型`string\|object`	要求選填	值與限制依模型而定	預設值—
欄位`prompt_cache_options`	類型`object`	要求選填	值與限制依模型而定	預設值—
欄位`modalities`	類型`string[]`	要求選填	值與限制 text	預設值—
欄位`provider`	類型`object`	要求選填	值與限制條件: 此 Ultra 文字路由拒絕 provider.options	預設值—
欄位`cache_control.ttl`	類型`string`	要求選填	值與限制條件: 實際提示詞快取位置拒絕 1h	預設值—

輸入素材組合

文字 · UTF8 文字

messages[].content

方法參數與生命週期

參數	規格
參數`content_type`	規格application/json
參數`required`	規格model, messages
參數`material_types`	規格text
參數`messages`	規格具有 role/content 結構的訊息陣列
參數`file_material`	規格實際 UTF-8 文件以原文文字插入；不推定檔案 ID
參數`optional_controls`	規格stream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice
參數`control_scope`	規格依模型而定；使用者函式工具與託管工具不同
參數`output_modalities`	規格text
參數`hosted_operations`	規格false
參數`multimodal_input_binding`	規格null

生命週期： synchronous_or_streamed

將確切模型 ID 與 messages 一併送出。

上下文長度不是輸出上限。

文字檔案可提供原文文字；圖片／音訊／檔案物件輸入需要精確綁定。

方案

費率資訊不會增加請求選項。支援的參數列於所選方法中。

快速模式: 標準費率選取快速模式時
flex 模式: 標準費率選取 flex 模式時
最高推理強度: 標準費率最高推理強度採用 default 調整值時
顯式快取計價: 未啟用請求顯式快取時

Ailoot 費率

各設定的價格

請逐一核對各項費用與所列條件。每組互為替代選項的設定只套用一種費率。

設定	費率表	上下文 Token 數大於	上下文 Token 數小於或等於	費用
設定詞元基本費率	費率表基本費率	上下文 Token 數大於—	上下文 Token 數小於或等於無上限	費用輸入 · 輸入 Token $0.004 / 1K Token 輸出 · 輸出 Token $0.024 / 1K Token 輸入 · 讀取快取 $0.0004 / 1K Token 輸入 · 寫入快取 $0.00075 / 1K Token 輸入 · 寫入快取，1 小時 — / 1K Token 輸出 · 圖片輸出詞元 $0.24 / 1K 圖片 Token
設定請求長度級距 1	費率表—	上下文 Token 數大於0	上下文 Token 數小於或等於無上限	費用輸入 · 輸入 Token $0.004 / 1K Token 輸出 · 輸出 Token $0.024 / 1K Token 輸入 · 讀取快取 $0.0004 / 1K Token 輸入 · 寫入快取 $0.00075 / 1K Token 輸入 · 寫入快取，1 小時 — / 1K Token

參考費率詳情

模型層級的參考費率表不會變更方法選擇、報價或費用。實際計費採用所選 Ailoot 方法的費率。

google/gemini-3-pro-image-preview

各設定的價格

設定	計費條件	費用
設定google/gemini-3-pro-image-preview · 預設設定	計費條件預設設定	費用輸入 · 輸入 US$0.004 / 1K Token 輸出 · 輸出 US$0.24 / 1K Token

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

各設定的價格

設定	計費條件	費用
設定Google: Nano Banana Pro (Gemini 3 Pro Image Preview) · 預設設定	計費條件預設設定	費用輸入 · 輸入 Token US$0.004 / 1K Token 輸出 · 輸出 Token US$0.024 / 1K Token 輸入 · 輸入圖片 US$0.004 / 1K Token 輸出 · 輸出圖片 US$0.24 / 1K Token 輸入 · 輸入音訊 US$0.004 / 1K Token 輸入 · 讀取音訊快取 US$0.0004 / 1K Token 輸出 · 網頁搜尋 US$0.028 每次搜尋輸出 · 推理詞元 US$0.024 / 1K Token 輸入 · 讀取快取 US$0.0004 / 1K Token 輸入 · 寫入快取 · 5 分鐘 US$0.00075 / 1K Token

Ailoot 會在請求前預留估算金額。取得結果後，依實測用量結算最終費用。串流結算採用最終用量紀錄。預留金額與結算的運作方式 →

請求範例

Ailoot Ultra · OpenAI Chat

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "google/gemini-3-pro-image-preview", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n    if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\n

from openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='google/gemini-3-pro-image-preview',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n    # Keep every field, including usage, IDs, reasoning and tool requests.\n    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n    print(json.dumps(data, ensure_ascii=False, indent=2))\n    def save_blob(encoded, suffix, label):\n        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n        ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n        file = Path(stem + "-" + label + "." + ext)\n        file.write_bytes(raw)\n        print("Saved", file)\n    for index, block in enumerate(data.get("content") or []):\n        print("Content", index, json.dumps(block, ensure_ascii=False))\n    for choice in data.get("choices") or []:\n        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n    for index, item in enumerate(data.get("output") or []):\n        print("Output", index, json.dumps(item, ensure_ascii=False))\n        if item.get("type") == "image_generation_call" and item.get("result"):\n            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n    for ci, candidate in enumerate(data.get("candidates") or []):\n        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n            blob = part.get("inlineData") or part.get("inline_data")\n            if blob and blob.get("data"):\n                mime = blob.get("mimeType") or blob.get("mime_type")\n                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\n

from openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n    # Keep every field, including usage, IDs, reasoning and tool requests.\n    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n    print(json.dumps(data, ensure_ascii=False, indent=2))\n    def save_blob(encoded, suffix, label):\n        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n        ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n        file = Path(stem + "-" + label + "." + ext)\n        file.write_bytes(raw)\n        print("Saved", file)\n    for index, block in enumerate(data.get("content") or []):\n        print("Content", index, json.dumps(block, ensure_ascii=False))\n    for choice in data.get("choices") or []:\n        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n    for index, item in enumerate(data.get("output") or []):\n        print("Output", index, json.dumps(item, ensure_ascii=False))\n        if item.get("type") == "image_generation_call" and item.get("result"):\n            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n    for ci, candidate in enumerate(data.get("candidates") or []):\n        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n            blob = part.get("inlineData") or part.get("inline_data")\n            if blob and blob.get("data"):\n                mime = blob.get("mimeType") or blob.get("mime_type")\n                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='google/gemini-3-pro-image-preview',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n        for event in stream:\n            payload=event.to_dict(mode="json")\n            log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n            print(json.dumps(payload,ensure_ascii=False))\n    final=stream.get_final_completion()\n    save_result(final.to_dict(mode="json"),"stream-result")\n

接收並儲存結果

Ailoot Ultra · OpenAI Chat

json 或 SSE

保留所有已完成的結果

請保留所有完整傳回的回應、檔案與內容區塊。

保留每個候選項及其內容／工具呼叫／推理。SSE 中依候選項／工具索引組合增量，保留最終完成原因與用量。

需要檢查原始回應時

保留請求／回應 ID 與標頭。未提供通用的文字工作擷取 URL。

回應欄位

欄位	類型
欄位`choices[].index`	類型integer
欄位`choices[].message.content`	類型string\|null
欄位`choices[].message.tool_calls`	類型tool_call[]
欄位`choices[].message.reasoning_content`	類型string
欄位`choices[].message.reasoning_details`	類型object\|array
欄位`choices[].finish_reason`	類型string\|null
欄位`usage`	類型object

錯誤與後續步驟

回應	條件	下一步
回應400 · `invalid_request_error`	條件必要欄位、素材表示方式或參數值無效	下一步提出新的建立請求前，先修正指定欄位。

別名

模型別名

Ailoot Ultra google/gemini-3-pro-image-preview

方法路徑別名

/v1/images

相關呼叫用 ID

google/gemini-3-pro-image-preview

", "method_identity": { "effective_request_model_id": "google/gemini-3-pro-image-preview", "fmt": "chat", "http": "POST", "path": "/v1/chat/completions", "request_model_id": "google/gemini-3-pro-image-preview", "route_label": "Ultra" }, "method_key": "Ultra|chat|/v1/chat/completions|google%2Fgemini-3-pro-image-preview", "ordinal": 0, "rawMethod": { "available": null, "cache_read": 0.4, "cache_write": 0.75, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "errors": [ { "code": null, "condition": "必要欄位、素材表示方式或參數值無效", "http_status": 400, "recovery": "提出新的建立請求前，先修正指定欄位。", "type": "invalid_request_error" } ], "hints": [ "Model context is not an output cap.", "A model-dependent field needs the exact published model profile; unspecified defaults/enums are not guessed.", "Tool calls propose actions; the client application returns actual observations with the returned call ID.", "Guarded canonical keys must occur once; duplicate or case-varied model/tools/cap fields are rejected." ], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/chat/completions" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "保留請求／回應 ID 與標頭。未提供通用的文字工作擷取 URL。" }, "material_combinations": [ { "mode": "text", "representation": "messages[].content", "required_material": "UTF8 文字" } ], "operation_modes": [ "conversation", "function_tool_continuation", "streaming" ], "outputs": { "all_results": "保留每個候選項及其內容／工具呼叫／推理。SSE 中依候選項／工具索引組合增量，保留最終完成原因與用量。", "family": "text", "fields": [ { "description": null, "name": "choices[].index", "type": "integer" }, { "description": null, "name": "choices[].message.content", "type": "string|null" }, { "description": null, "name": "choices[].message.tool_calls", "type": "tool_call[]" }, { "description": null, "name": "choices[].message.reasoning_content", "type": "string" }, { "description": null, "name": "choices[].message.reasoning_details", "type": "object|array" }, { "description": null, "name": "choices[].finish_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json 或 SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "google/gemini-3-pro-image-preview" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "messages", "required": true, "type": "message[]" }, { "availability": "model_dependent", "conditions": "這些角色由原始碼中的相容型別明確表示；確切角色集合仍依模型而定。", "default": null, "default_known": false, "enum": [ "system", "user", "assistant", "tool", "function" ], "enum_exhaustive": false, "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": false, "type": "string|content_part[]|null" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].tool_calls", "required": false, "type": "tool_call[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].tool_call_id", "required": false, "type": "string", "when": "role=tool；使用實際回傳的 ID" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].name", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].reasoning_content", "required": false, "type": "string", "when": "此模型使用推理內容時，將實際回傳的推理內容帶回後續請求" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].reasoning_details", "required": false, "type": "object|array" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "max_tokens", "required": false, "type": "integer" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "max_completion_tokens", "required": false, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "stream_options.include_usage", "required": false, "type": "boolean" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "temperature", "required": false, "type": "number" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "top_p", "required": false, "type": "number" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "stop", "required": false, "type": "string|string[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools", "required": false, "type": "function_tool[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": [ "function" ], "enum_known": true, "name": "tools[].type", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools[].function.name", "required": true, "type": "string", "when": "已提供函式工具" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools[].function.description", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools[].function.parameters", "required": false, "type": "JSON_schema" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tool_choice", "required": false, "type": "string|object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "parallel_tool_calls", "required": false, "type": "boolean" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "response_format", "required": false, "type": "object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "reasoning_effort", "required": false, "type": "string" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "reasoning", "required": false, "type": "object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "service_tier", "required": false, "type": "string" }, { "availability": "source_compatibility", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "instructions", "required": false, "type": "string" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "functions", "required": false, "type": "function[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "function_call", "required": false, "type": "string|object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "prompt_cache_options", "required": false, "type": "object" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "text" ], "enum_known": true, "name": "modalities", "required": false, "type": "string[]" }, { "availability": "source_interface", "conditions": "此 Ultra 文字路由拒絕 provider.options", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "object" }, { "availability": "source_interface", "conditions": "實際提示詞快取位置拒絕 1h", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "cache_control.ttl", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "additional models/models list", "plugins", "route", "hosted tools; ordinary client function tools remain separate", "non-text output modalities", "provider.options overrides", "one-hour prompt-cache TTL" ] }, "hints": [ "將確切模型 ID 與 messages 一併送出。", "上下文長度不是輸出上限。", "文字檔案可提供原文文字；圖片／音訊／檔案物件輸入需要精確綁定。" ], "input": { "content_type": "application/json", "control_scope": "依模型而定；使用者函式工具與託管工具不同", "file_material": "實際 UTF-8 文件以原文文字插入；不推定檔案 ID", "hosted_operations": false, "material_types": [ "text" ], "messages": "具有 role/content 結構的訊息陣列", "multimodal_input_binding": null, "optional_controls": [ "stream", "temperature", "top_p", "stop", "max_tokens", "max_completion_tokens", "reasoning", "response_format", "tools", "tool_choice" ], "output_modalities": [ "text" ], "required": [ "model", "messages" ] }, "lifecycle": { "mode": "synchronous_or_streamed", "unknown": "preserve original request id; no generic chat job retrieval path" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [], "operation_modes": [ "text_chat" ], "output": { "content": "choices[].message.content or stream delta", "family": "text", "format": "json or supported SSE", "tool_calls": "retain actual returned function calls" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "詞元基本費率", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "dimensions": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "請求長度級距 1", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "declared_tariff_references": [ { "client_selectable": false, "key": "dd403f513dffcdc5a75d", "label": "google/gemini-3-pro-image-preview", "profiles": [ { "basis": "", "dimensions": { "conditions": "預設設定" }, "label": "google/gemini-3-pro-image-preview · 預設設定", "units": [ { "amount": "0.004", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "e6957656f069545cb1e5fe01", "label": "Input", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "tokens" }, { "amount": "0.24", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "2e51394f953e56115402542f", "label": "Output", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "tokens" } ] } ] }, { "client_selectable": false, "key": "3ba80d11c6a9d6a9a06e", "label": "Google: Nano Banana Pro (Gemini 3 Pro Image Preview)", "profiles": [ { "basis": "", "dimensions": { "conditions": "預設設定" }, "label": "Google: Nano Banana Pro (Gemini 3 Pro Image Preview) · 預設設定", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "6da94d9d3d98173f5e6e9c66", "label": "Input tokens", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "1a45fdb7264f6279403e9086", "label": "Output tokens", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "output_token" }, { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "bc57ce11b8664d047f995550", "label": "Input image", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_image_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "ad0457f9fab1294591cda73f", "label": "Output image", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "output_image_token" }, { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "8eb63c3adff16c514a8fb844", "label": "Audio input", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_audio_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "0b6ad5dff6255850f2934265", "label": "Audio cache read", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "cached_audio_input_token" }, { "amount": "0.028", "amount_unit": "usd_per_exact_physical_unit", "billing_input": false, "key": "5810c5c1ff81f13134623408", "label": "Web search", "public_unit_label": "per search", "quantity": "1", "rate_role": "declared_tariff_rate", "side": "out", "unit": "web_search" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "6d275a019972ae8656d9333a", "label": "Reasoning tokens", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "internal_reasoning_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "92b31eda5cb5bd9931a89643", "label": "Cache read", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.000750000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "461b53e0807f50a40039778e", "label": "Cache write · 5 minutes", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "cache_write_token" } ] } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"google/gemini-3-pro-image-preview\", \"messages\": [{\"role\": \"user\", \"content\": \"Explain the difference between an observation and a hypothesis in three sentences.\"}]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model='google/gemini-3-pro-image-preview',messages=[{\"role\":\"user\",\"content\":\"Explain observations and hypotheses in three sentences.\"}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n", "label": "Python SDK · all choices", "language": "python" }, { "code": "from openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nwith client.chat.completions.stream(model='google/gemini-3-pro-image-preview',messages=[{\"role\":\"user\",\"content\":\"Explain observations and hypotheses in three sentences.\"}]) as stream:\n with Path(\"stream-events.jsonl\").open(\"w\",encoding=\"utf-8\") as log:\n for event in stream:\n payload=event.to_dict(mode=\"json\")\n log.write(json.dumps(payload,ensure_ascii=False)+\"\\n\"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode=\"json\"),\"stream-result\")\n", "label": "Python SDK streaming · all events", "language": "python" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "chat", "hints": [ "Send exact model ID with messages.", "Context length is not an output cap.", "A text file may supply literal text; image/audio/file-object inputs need exact binding." ], "http": "POST", "in": 4, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 24, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "快速模式", "value": "標準費率", "when": "選取快速模式時" }, { "label": "flex 模式", "value": "標準費率", "when": "選取 flex 模式時" }, { "label": "最高推理強度", "value": "標準費率", "when": "最高推理強度採用 default 調整值時" }, { "label": "顯式快取計價", "value": "未啟用", "when": "請求顯式快取時" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": 0.00024, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "dimensions": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "google/gemini-3-pro-image-preview", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": 0.4, "cache_read_multiplier": null, "cache_write": 0.75, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 4, "input_multiplier": null, "interval_boundary": "min_exclusive_max_inclusive", "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "null_rate_policy": "unknown or inherited; never zero", "out": 24, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] } }, { "html": "

G

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

Google /google/gemini-3-pro-image-preview

入門

Nano Banana Pro 是 Google 用於圖像生成及編輯的 Gemini 3 Pro Image Preview 模型。文件記載的設定支援多圖構圖、內嵌文字、局部創意修改、Search grounding，以及 2K 或 4K 輸出。

接受的輸入: 文字 · 圖片
結果: 圖片

關於模型

此說明介紹模型的能力。Ailoot 可用的輸入、輸出與選項，依所選方法列於下方。

Nano Banana Pro 是 Google 以 Gemini 3 Pro 為基礎的圖像生成及編輯模型。它接收文字與圖像，回傳圖像或文字。公開設定涵蓋資訊圖表、圖解、產品視覺、分鏡，以及結合多個元素或參考的構圖。

規劃圖像及其可見內容

說明用途、主要主體及元素關係。資訊圖表應提供實際數據及確切標籤，再描述層級。產品場景應指明物件、視角、環境及必須保持可辨識的細節。模型文件記載的文字呈現用途包含較長措辭及多語版面；請檢查圖像中回傳的字母、數字及對齊。

文件記載 Search grounding 可將最新資訊納入視覺任務。介面提供此能力時，應隨需求保留來源事實，並檢查它們如何呈現在最終圖像。視覺上令人信服的圖表或圖解，仍應與預定呈現的資料及標籤比較。

參考與創意編輯

公開代管設定接受最多十四張參考圖像。請為每張指定角色，例如主體、環境或視覺風格，而非不說明彼此關係。模型描述強調最多五個主體的身分保留；應在整體回傳構圖中比較實際主體及重要細節。

編輯時應指出要修改的區域或物件，以及要保留的元素。文件中的創意控制包括局部修改、光線與焦點調整、鏡頭變換及彈性長寬比。檢查所需編輯及應保持可辨識的細節時，請保留來源圖像。

解析度與可用成果

此設定記載 2K 及 4K 輸出。請依預期放置位置選擇視覺比例及輸出規模，再檢查實際回傳尺寸與檔案屬性。圖像進入較大版面時，設計概念與匯出要求應保持連結。

將選定圖像與需求、參考角色及修訂歷史一起保留。以素材實際使用尺寸檢查確切文字、主體連續性及資訊層級。實用交付成果是此模型設定經檢查的視覺素材或附帶文字，可供後續設計或製作。

Ailoot 可用 · 輸入與方法

Ailoot Ultra · 圖片 POST /v1/images/generations

輸入: text, image
輸出: image

欄位	類型	要求	值與限制	預設值
欄位`model`	類型`string`	要求必填	值與限制 google/gemini-3-pro-image-preview	預設值—
欄位`prompt`	類型`string`	要求必填	值與限制長度下限: 1	預設值—
欄位`resolution`	類型`string`	要求選填	值與限制 1K, 2K, 4K	預設值—
欄位`aspect_ratio`	類型`string`	要求選填	值與限制 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9	預設值—
欄位`n`	類型`integer`	要求選填	值與限制最小值: 1 最大值: 1	預設值—
欄位`input_references`	類型`array`	要求選填	值與限制項目數下限: 0 項目數上限: 14	預設值—
欄位`provider`	類型`object`	要求選填	值與限制	預設值—

方法參數與生命週期

參數	規格
參數`content_type`	規格application/json
參數`required`	規格model, prompt
參數`material_types`	規格text, image

生命週期： synchronous_result_then_owned_library

GET /v1/media/tasks/{task_id} · 實際回傳的 task_id 或媒體庫紀錄

GET /v1/media/assets/{asset_id}/content

GET /v1/media/assets

GET /v1/media/assets/{asset_id}

方案

最終費用的計算方式

顯示的費率用於在生成前估算費用。最終費用依原始生成作業記錄的成本計算。實測的輸出時間長度與預估數量並非最終計費數量。

費率資訊不會增加請求選項。支援的參數列於所選方法中。

快速模式: 未指定選取快速模式時
flex 模式: 未指定選取 flex 模式時
最高推理強度: 未指定最高推理強度採用 default 調整值時

供比較的預估費率

各設定的價格

請逐一核對各項費用與所列條件。每組互為替代選項的設定只套用一種費率。

設定	費率表	預估數量	費用
設定基礎 token 費率	費率表基本費率	預估數量—	費用輸入 · 輸入 Token · 僅供比較的估算 — / 1K Token 輸出 · 輸出 Token · 僅供比較的估算 — / 1K Token 輸入 · 讀取快取 · 僅供比較的估算 — / 1K Token 輸入 · 寫入快取 · 僅供比較的估算 — / 1K Token 輸入 · 寫入快取，1 小時 · 僅供比較的估算 — / 1K Token
設定預估生成費用	費率表—	預估數量單次生成估算值	費用輸出 · 預估生成費用 · 僅供比較的估算 $0.98304 生成前估算此估算並非額外費用，也不是本次生成完成後的固定費率。

Ailoot 會在請求前預留估算金額。取得結果後，依實測用量結算最終費用。串流結算採用最終用量紀錄。預留金額與結算的運作方式 →

請求範例

Ailoot Ultra · 圖片

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'google/gemini-3-pro-image-preview', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n    if not items: raise SystemExit("No completed images; retain the full response")\n    for index, item in enumerate(items):\n        encoded=item.get("b64_json")\n        uri=item.get("url", "")\n        if not encoded and uri.startswith("data:image/"):\n            header, separator, encoded=uri.partition(",")\n            if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")\n        if encoded:\n            raw=base64.b64decode(encoded,validate=True)\n        elif uri:\n            uri=urljoin("https://api.ailoot.ai",uri)\n            parsed=urlsplit(uri)\n            if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:\n                raise SystemExit("Invalid returned image URL")\n            owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")\n            args=["curl","--fail-with-body","--silent","--show-error",uri]\n            if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]\n            temporary=Path(f"image-{index}.bin")\n            subprocess.run(args+["-o",str(temporary)],check=True)\n            raw=temporary.read_bytes()\n            temporary.unlink()\n        else: raise SystemExit("Image has neither bytes nor URL")\n        if not raw: raise SystemExit("Empty image result")\n        ext="png" if raw.startswith(b"\\x89PNG") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else "svg" if raw.lstrip().startswith((b"<svg",b"<?xml")) else "bin"\n        Path(f"image-{index}.{ext}").write_bytes(raw)\n    print("Every returned image saved; complete response metadata retained")\nsave_images(json.loads(Path("result.json").read_text())["data"])\nPY_RESULT\n

接收並儲存結果

Ailoot Ultra · 圖片

下載每張傳回的圖片，或儲存其 Base64 內容

GET /v1/media/tasks/{task_id}
GET /v1/media/assets/{asset_id}/content
GET /v1/media/assets
GET /v1/media/assets/{asset_id}

其他內容讀取方式

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\n

保留所有已完成的結果

請儲存每張已完成的圖片；從每個傳回的 URL 取得圖片，或解碼其 Base64 內容。

保留完整回應與標頭、所有實際輸出、原始回傳的識別碼，以及所有用量欄位。恢復原始作業時，請勿再次提交建立請求。

需要檢查原始回應時

保留回傳的 X-Client-Request-ID，以供核對。僅讀取實際回傳的 task_id/asset_id 值或自己擁有的媒體庫；請勿以作業 ID 組成任務 URL，也請勿重新提交建立請求。

回應欄位

欄位	類型
欄位`created`	類型
欄位`data[].url`	類型
欄位`data[].b64_json`	類型
欄位`usage`	類型

別名

模型別名

Ailoot Ultra google/gemini-3-pro-image-preview

方法路徑別名

/v1/images

相關呼叫用 ID

google/gemini-3-pro-image-preview

", "method_identity": { "effective_request_model_id": "google/gemini-3-pro-image-preview", "fmt": "images", "http": "POST", "path": "/v1/images/generations", "request_model_id": "google/gemini-3-pro-image-preview", "route_label": "Ultra" }, "method_key": "Ultra|images|/v1/images/generations|google%2Fgemini-3-pro-image-preview", "ordinal": 1, "rawMethod": { "available": null, "billing_basis": { "basis": "original_invoice_usd", "description": "顯示的費率用於在生成前估算費用。最終費用依原始生成作業記錄的成本計算。實測的輸出時間長度與預估數量並非最終計費數量。", "forecast_quantity_is_estimate": true, "physical_output_is_billable_quantity": false, "version": "original_invoice_usd_v1" }, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "actual returned task_id or media library record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "保留回傳的 X-Client-Request-ID，以供核對。僅讀取實際回傳的 task_id/asset_id 值或自己擁有的媒體庫；請勿以作業 ID 組成任務 URL，也請勿重新提交建立請求。" }, "outputs": { "all_results": "保留完整回應與標頭、所有實際輸出、原始回傳的識別碼，以及所有用量欄位。恢復原始作業時，請勿再次提交建立請求。", "family": "image", "fields": [ { "description": null, "name": "created", "type": null }, { "description": null, "name": "data[].url", "type": null }, { "description": null, "name": "data[].b64_json", "type": null }, { "description": null, "name": "usage", "type": null } ] }, "parameters": [ { "enum": [ "google/gemini-3-pro-image-preview" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "min_length": 1, "name": "prompt", "required": true, "type": "string" }, { "enum": [ "1K", "2K", "4K" ], "enum_known": true, "name": "resolution", "required": false, "type": "string" }, { "enum": [ "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9" ], "enum_known": true, "name": "aspect_ratio", "required": false, "type": "string" }, { "maximum": 1, "minimum": 1, "name": "n", "required": false, "type": "integer" }, { "items": { "properties": { "image_url": { "properties": { "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": { "const": "image_url" } }, "required": [ "type", "image_url" ], "type": "object" }, "maximum_items": 14, "minimum_items": 0, "name": "input_references", "required": false, "type": "array" }, { "name": "provider", "required": false, "type": "object" } ], "request_schema": { "properties": { "aspect_ratio": { "enum": [ "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9" ], "type": "string" }, "input_references": { "items": { "properties": { "image_url": { "properties": { "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": { "const": "image_url" } }, "required": [ "type", "image_url" ], "type": "object" }, "maxItems": 14, "minItems": 0, "type": "array" }, "model": { "enum": [ "google/gemini-3-pro-image-preview" ], "type": "string" }, "n": { "maximum": 1, "minimum": 1, "type": "integer" }, "prompt": { "minLength": 1, "type": "string" }, "provider": { "description": "Exact image provider preferences from the documented image request schema.", "type": "object" }, "resolution": { "enum": [ "1K", "2K", "4K" ], "type": "string" } }, "required": [ "model", "prompt" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": false, "body": { "model": "google/gemini-3-pro-image-preview", "prompt": "a red panda astronaut floating in space, studio lighting" }, "content_type": "application/json" } }, "input": { "content_type": "application/json", "material_types": [ "text", "image" ], "required": [ "model", "prompt" ] }, "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "實際回傳的 task_id 或媒體庫紀錄", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation." }, "method": { "http": "POST", "path": "/v1/images/generations" }, "output": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "family": "image", "fields": [ { "description": null, "name": "created", "type": null }, { "description": null, "name": "data[].url", "type": null }, { "description": null, "name": "data[].b64_json", "type": null }, { "description": null, "name": "usage", "type": null } ] } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "basis": "此估算並非額外費用，也不是本次生成完成後的固定費率。", "dimensions": { "forecast_quantity": "one generation estimate" }, "label": "預估生成費用", "units": [ { "amount": "0.98304", "amount_unit": "usd_per_exact_native_unit", "billing_input": false, "estimate_only": true, "key": "forecast_usd", "label": "Estimated generation cost", "native_amount_usd_per_unit": "0.98304", "native_quantity": 1, "public_unit_label": "estimate before generation", "quantity": 1, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "request_estimate" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'google/gemini-3-pro-image-preview', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit(\"No completed images; retain the full response\")\n for index, item in enumerate(items):\n encoded=item.get(\"b64_json\")\n uri=item.get(\"url\", \"\")\n if not encoded and uri.startswith(\"data:image/\"):\n header, separator, encoded=uri.partition(\",\")\n if not separator or \";base64\" not in header: raise SystemExit(\"Unsupported image data URI\")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin(\"https://api.ailoot.ai\",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!=\"https\" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit(\"Invalid returned image URL\")\n owned=parsed.netloc==\"api.ailoot.ai\" and parsed.path.startswith(\"/v1/media/assets/\") and parsed.path.endswith(\"/content\")\n args=[\"curl\",\"--fail-with-body\",\"--silent\",\"--show-error\",uri]\n if owned: args += [\"-H\",\"Authorization: Bearer \"+os.environ[\"AILOOT_API_KEY\"]]\n temporary=Path(f\"image-{index}.bin\")\n subprocess.run(args+[\"-o\",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit(\"Image has neither bytes nor URL\")\n if not raw: raise SystemExit(\"Empty image result\")\n ext=\"png\" if raw.startswith(b\"\\x89PNG\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4]==b\"RIFF\" and raw[8:12]==b\"WEBP\" else \"svg\" if raw.lstrip().startswith((b\"G

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

Google /google/gemini-3-pro-image-preview

入門

Nano Banana Pro 是 Google 用於圖像生成及編輯的 Gemini 3 Pro Image Preview 模型。文件記載的設定支援多圖構圖、內嵌文字、局部創意修改、Search grounding，以及 2K 或 4K 輸出。

接受的輸入: 文字
結果: json · sse

關於模型

此說明介紹模型的能力。Ailoot 可用的輸入、輸出與選項，依所選方法列於下方。

Nano Banana Pro 是 Google 以 Gemini 3 Pro 為基礎的圖像生成及編輯模型。它接收文字與圖像，回傳圖像或文字。公開設定涵蓋資訊圖表、圖解、產品視覺、分鏡，以及結合多個元素或參考的構圖。

規劃圖像及其可見內容

說明用途、主要主體及元素關係。資訊圖表應提供實際數據及確切標籤，再描述層級。產品場景應指明物件、視角、環境及必須保持可辨識的細節。模型文件記載的文字呈現用途包含較長措辭及多語版面；請檢查圖像中回傳的字母、數字及對齊。

文件記載 Search grounding 可將最新資訊納入視覺任務。介面提供此能力時，應隨需求保留來源事實，並檢查它們如何呈現在最終圖像。視覺上令人信服的圖表或圖解，仍應與預定呈現的資料及標籤比較。

參考與創意編輯

公開代管設定接受最多十四張參考圖像。請為每張指定角色，例如主體、環境或視覺風格，而非不說明彼此關係。模型描述強調最多五個主體的身分保留；應在整體回傳構圖中比較實際主體及重要細節。

編輯時應指出要修改的區域或物件，以及要保留的元素。文件中的創意控制包括局部修改、光線與焦點調整、鏡頭變換及彈性長寬比。檢查所需編輯及應保持可辨識的細節時，請保留來源圖像。

解析度與可用成果

此設定記載 2K 及 4K 輸出。請依預期放置位置選擇視覺比例及輸出規模，再檢查實際回傳尺寸與檔案屬性。圖像進入較大版面時，設計概念與匯出要求應保持連結。

將選定圖像與需求、參考角色及修訂歷史一起保留。以素材實際使用尺寸檢查確切文字、主體連續性及資訊層級。實用交付成果是此模型設定經檢查的視覺素材或附帶文字，可供後續設計或製作。

Ailoot 可用 · 輸入與方法

Ailoot Ultra · Anthropic Messages POST /v1/messages

輸入: text
輸出: json, sse
輸出格式: JSON 或 SSE

欄位	類型	要求	值與限制	預設值
欄位`model`	類型`string`	要求必填	值與限制 google/gemini-3-pro-image-preview	預設值—
欄位`messages`	類型`message[]`	要求必填	值與限制	預設值—
欄位`messages[].role`	類型`string`	要求必填	值與限制 user, assistant	預設值—
欄位`messages[].content`	類型`string`	要求必填	值與限制	預設值—
欄位`max_tokens`	類型`integer`	要求必填	值與限制	預設值—
欄位`stream`	類型`boolean`	要求選填	值與限制	預設值false

輸入素材組合

文字 · UTF-8 文字

messages[].content 字串

方法參數與生命週期

參數	規格
參數`content_type`	規格application/json
參數`required`	規格model, messages, max_tokens
參數`optional_controls`	規格stream
參數`material_types`	規格text
參數`text_file_material`	規格讀取實際 UTF-8 文件，將文字放入指定的 input 或訊息內容字串。

生命週期： synchronous_or_streamed

方案

費率資訊不會增加請求選項。支援的參數列於所選方法中。

快速模式: 標準費率選取快速模式時
flex 模式: 標準費率選取 flex 模式時
最高推理強度: 標準費率最高推理強度採用 default 調整值時
顯式快取計價: 未啟用請求顯式快取時

Ailoot 費率

各設定的價格

請逐一核對各項費用與所列條件。每組互為替代選項的設定只套用一種費率。

設定	費率表	上下文 Token 數大於	上下文 Token 數小於或等於	費用
設定詞元基本費率	費率表基本費率	上下文 Token 數大於—	上下文 Token 數小於或等於無上限	費用輸入 · 輸入 Token $0.004 / 1K Token 輸出 · 輸出 Token $0.024 / 1K Token 輸入 · 讀取快取 $0.0004 / 1K Token 輸入 · 寫入快取 $0.00075 / 1K Token 輸入 · 寫入快取，1 小時 — / 1K Token 輸出 · 圖片輸出詞元 $0.24 / 1K 圖片 Token
設定請求長度級距 1	費率表—	上下文 Token 數大於0	上下文 Token 數小於或等於無上限	費用輸入 · 輸入 Token $0.004 / 1K Token 輸出 · 輸出 Token $0.024 / 1K Token 輸入 · 讀取快取 $0.0004 / 1K Token 輸入 · 寫入快取 $0.00075 / 1K Token 輸入 · 寫入快取，1 小時 — / 1K Token

Ailoot 會在請求前預留估算金額。取得結果後，依實測用量結算最終費用。串流結算採用最終用量紀錄。預留金額與結算的運作方式 →

請求範例

Ailoot Ultra · Anthropic Messages

{\n  "model": "google/gemini-3-pro-image-preview",\n  "max_tokens": 128,\n  "messages": [\n    {\n      "role": "user",\n      "content": "Summarize the incident report."\n    }\n  ],\n  "stream": false\n}

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n    raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemini-3-pro-image-preview', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n

import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n    raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemini-3-pro-image-preview', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n    raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n    with urlopen(request, timeout=120) as response:\n        raw = response.read()\n        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n    Path('result.json').write_bytes(error.read())\n    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n    raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n

接收並儲存結果

Ailoot Ultra · Anthropic Messages

JSON 或 SSE

保留所有已完成的結果

請保留所有完整傳回的回應、檔案與內容區塊。

保留所有內容區塊、思考簽章與實際工具使用 ID。組合所有 SSE 區塊增量；第一個文字區塊不是完整輸出。

需要檢查原始回應時

保留請求／回應 ID 與標頭。未提供通用的文字工作擷取 URL。

回應欄位

欄位	類型
欄位`id`	類型string
欄位`model`	類型string
欄位`content[]`	類型text\|thinking\|tool_use\|other content block
欄位`stop_reason`	類型string\|null
欄位`usage`	類型object

錯誤與後續步驟

回應	條件	下一步
回應400 · `invalid_request_error`	條件必要欄位、素材表示方式或參數值無效	下一步提出新的建立請求前，先修正指定欄位。

別名

模型別名

Ailoot Ultra google/gemini-3-pro-image-preview

方法路徑別名

/v1/images

相關呼叫用 ID

google/gemini-3-pro-image-preview

", "method_identity": { "effective_request_model_id": "google/gemini-3-pro-image-preview", "fmt": "messages", "http": "POST", "path": "/v1/messages", "request_model_id": "google/gemini-3-pro-image-preview", "route_label": "Ultra" }, "method_key": "Ultra|messages|/v1/messages|google%2Fgemini-3-pro-image-preview", "ordinal": 2, "rawMethod": { "available": null, "cache_read": 0.4, "cache_write": 0.75, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "errors": [ { "code": null, "condition": "必要欄位、素材表示方式或參數值無效", "http_status": 400, "recovery": "提出新的建立請求前，先修正指定欄位。", "type": "invalid_request_error" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "google/gemini-3-pro-image-preview", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "保留請求／回應 ID 與標頭。未提供通用的文字工作擷取 URL。" }, "material_combinations": [ { "mode": "text", "representation": "messages[].content 字串", "required_material": "UTF-8 文字" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "保留所有內容區塊、思考簽章與實際工具使用 ID。組合所有 SSE 區塊增量；第一個文字區塊不是完整輸出。", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON 或 SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "google/gemini-3-pro-image-preview" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "google/gemini-3-pro-image-preview" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "google/gemini-3-pro-image-preview", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "讀取實際 UTF-8 文件，將文字放入指定的 input 或訊息內容字串。" }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "詞元基本費率", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "dimensions": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "請求長度級距 1", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "{\n \"model\": \"google/gemini-3-pro-image-preview\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemini-3-pro-image-preview', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemini-3-pro-image-preview', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "messages", "http": "POST", "in": 4, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 24, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "快速模式", "value": "標準費率", "when": "選取快速模式時" }, { "label": "flex 模式", "value": "標準費率", "when": "選取 flex 模式時" }, { "label": "最高推理強度", "value": "標準費率", "when": "最高推理強度採用 default 調整值時" }, { "label": "顯式快取計價", "value": "未啟用", "when": "請求顯式快取時" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": 0.00024, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "dimensions": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "google/gemini-3-pro-image-preview", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": 0.4, "cache_read_multiplier": null, "cache_write": 0.75, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 4, "input_multiplier": null, "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "out": 24, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] } } ], "model_id": "google/gemini-3-pro-image-preview", "nativeProvenance": { "input_facts_canonical_sha256": "d10fcac924fbb406d18c6f8a05ff8fee665b48b85c52d5fdbddb8dc774c71401", "input_html_sha256": "fbe59c16d0f558e35f75f10a7f962cd32a09593a7a7dfac416a89e919c1a0949", "private_audit_sha256": "be81c93523f0573278eb88f7bc13135587d317655ced386bdfc0146b2a686515", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/zh-hant-tw/google-gemini-3-pro-image-preview/", "description": "Nano Banana Pro 是 Google 用於圖像生成及編輯的 Gemini 3 Pro Image Preview 模型。文件記載的設定支援多圖構圖、內嵌文字、局部創意修改、Search grounding，以及 2K 或 4K 輸出。", "locale": "zh-hant-tw", "md_url": "https://ailoot.ai/models/zh-hant-tw/google-gemini-3-pro-image-preview/index.md", "title": "Google: Nano Banana Pro (Gemini 3 Pro Image Preview) — Ailoot", "url": "https://ailoot.ai/models/zh-hant-tw/google-gemini-3-pro-image-preview/" }, "practicalHtml": "

實用任務

英文技術範例

Create a stock comparison infographic\n\nCreate a clean landscape infographic for a warehouse noticeboard. Title: "Weekly stock". Show exactly three labelled bars: "Boxes 24", "Bags 12", "Tubes 6". Use a white background, dark text and one blue accent. Keep labels large, align the bars to a common baseline and add no extra numbers or logos.

預期結果

One infographic image with the exact title and three proportional, labelled bars on a common baseline.

檢查結果

Inspect all lettering and confirm the bar values and relative lengths match 24, 12 and 6.

", "practicalText": "實用任務英文技術範例 Create a stock comparison infographic Create a clean landscape infographic for a warehouse noticeboard. Title: \"Weekly stock\". Show exactly three labelled bars: \"Boxes 24\", \"Bags 12\", \"Tubes 6\". Use a white background, dark text and one blue accent. Keep labels large, align the bars to a common baseline and add no extra numbers or logos. 預期結果 One infographic image with the exact title and three proportional, labelled bars on a common baseline. 檢查結果 Inspect all lettering and confirm the bar values and relative lengths match 24, 12 and 6.", "rawModel": { "about": "Nano Banana Pro 是 Google 以 Gemini 3 Pro 為基礎的圖像生成及編輯模型。它接收文字與圖像，回傳圖像或文字。公開設定涵蓋資訊圖表、圖解、產品視覺、分鏡，以及結合多個元素或參考的構圖。\n\n### 規劃圖像及其可見內容\n\n說明用途、主要主體及元素關係。資訊圖表應提供實際數據及確切標籤，再描述層級。產品場景應指明物件、視角、環境及必須保持可辨識的細節。模型文件記載的文字呈現用途包含較長措辭及多語版面；請檢查圖像中回傳的字母、數字及對齊。\n\n文件記載 Search grounding 可將最新資訊納入視覺任務。介面提供此能力時，應隨需求保留來源事實，並檢查它們如何呈現在最終圖像。視覺上令人信服的圖表或圖解，仍應與預定呈現的資料及標籤比較。\n\n### 參考與創意編輯\n\n公開代管設定接受最多十四張參考圖像。請為每張指定角色，例如主體、環境或視覺風格，而非不說明彼此關係。模型描述強調最多五個主體的身分保留；應在整體回傳構圖中比較實際主體及重要細節。\n\n編輯時應指出要修改的區域或物件，以及要保留的元素。文件中的創意控制包括局部修改、光線與焦點調整、鏡頭變換及彈性長寬比。檢查所需編輯及應保持可辨識的細節時，請保留來源圖像。\n\n### 解析度與可用成果\n\n此設定記載 2K 及 4K 輸出。請依預期放置位置選擇視覺比例及輸出規模，再檢查實際回傳尺寸與檔案屬性。圖像進入較大版面時，設計概念與匯出要求應保持連結。\n\n將選定圖像與需求、參考角色及修訂歷史一起保留。以素材實際使用尺寸檢查確切文字、主體連續性及資訊層級。實用交付成果是此模型設定經檢查的視覺素材或附帶文字，可供後續設計或製作。", "alias_of": null, "aliases": [], "author": "Google", "available": null, "canonical_model": "google/gemini-3-pro-image-preview", "capabilities": { "context_length": 65536, "manufacturer_input": [ "image", "text" ], "manufacturer_output": [ "image", "text" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "configuration": "OpenAI Chat", "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "configuration": "OpenAI Chat", "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.98304", "amount_unit": "usd_per_exact_native_unit", "billing_input": false, "configuration": "Images", "estimate_only": true, "key": "forecast_usd", "label": "Estimated generation cost", "native_amount_usd_per_unit": "0.98304", "native_quantity": 1, "public_unit_label": "estimate before generation", "quantity": 1, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "request_estimate" }, { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "configuration": "Anthropic Messages", "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "configuration": "Anthropic Messages", "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "完整的設定價格資訊請見詳細資料。" } ], "ctx": null, "entity_type": "model", "formats": [ "chat", "images", "messages" ], "id": "google/gemini-3-pro-image-preview", "input": [ "text", "image" ], "kind": "text", "max_out": null, "methods": [ { "available": null, "cache_read": 0.4, "cache_write": 0.75, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "errors": [ { "code": null, "condition": "必要欄位、素材表示方式或參數值無效", "http_status": 400, "recovery": "提出新的建立請求前，先修正指定欄位。", "type": "invalid_request_error" } ], "hints": [ "Model context is not an output cap.", "A model-dependent field needs the exact published model profile; unspecified defaults/enums are not guessed.", "Tool calls propose actions; the client application returns actual observations with the returned call ID.", "Guarded canonical keys must occur once; duplicate or case-varied model/tools/cap fields are rejected." ], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/chat/completions" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "保留請求／回應 ID 與標頭。未提供通用的文字工作擷取 URL。" }, "material_combinations": [ { "mode": "text", "representation": "messages[].content", "required_material": "UTF8 文字" } ], "operation_modes": [ "conversation", "function_tool_continuation", "streaming" ], "outputs": { "all_results": "保留每個候選項及其內容／工具呼叫／推理。SSE 中依候選項／工具索引組合增量，保留最終完成原因與用量。", "family": "text", "fields": [ { "description": null, "name": "choices[].index", "type": "integer" }, { "description": null, "name": "choices[].message.content", "type": "string|null" }, { "description": null, "name": "choices[].message.tool_calls", "type": "tool_call[]" }, { "description": null, "name": "choices[].message.reasoning_content", "type": "string" }, { "description": null, "name": "choices[].message.reasoning_details", "type": "object|array" }, { "description": null, "name": "choices[].finish_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json 或 SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "google/gemini-3-pro-image-preview" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "messages", "required": true, "type": "message[]" }, { "availability": "model_dependent", "conditions": "這些角色由原始碼中的相容型別明確表示；確切角色集合仍依模型而定。", "default": null, "default_known": false, "enum": [ "system", "user", "assistant", "tool", "function" ], "enum_exhaustive": false, "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": false, "type": "string|content_part[]|null" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].tool_calls", "required": false, "type": "tool_call[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].tool_call_id", "required": false, "type": "string", "when": "role=tool；使用實際回傳的 ID" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].name", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].reasoning_content", "required": false, "type": "string", "when": "此模型使用推理內容時，將實際回傳的推理內容帶回後續請求" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].reasoning_details", "required": false, "type": "object|array" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "max_tokens", "required": false, "type": "integer" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "max_completion_tokens", "required": false, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "stream_options.include_usage", "required": false, "type": "boolean" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "temperature", "required": false, "type": "number" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "top_p", "required": false, "type": "number" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "stop", "required": false, "type": "string|string[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools", "required": false, "type": "function_tool[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": [ "function" ], "enum_known": true, "name": "tools[].type", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools[].function.name", "required": true, "type": "string", "when": "已提供函式工具" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools[].function.description", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tools[].function.parameters", "required": false, "type": "JSON_schema" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "tool_choice", "required": false, "type": "string|object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "parallel_tool_calls", "required": false, "type": "boolean" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "response_format", "required": false, "type": "object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "reasoning_effort", "required": false, "type": "string" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "reasoning", "required": false, "type": "object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "service_tier", "required": false, "type": "string" }, { "availability": "source_compatibility", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "instructions", "required": false, "type": "string" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "functions", "required": false, "type": "function[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "function_call", "required": false, "type": "string|object" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "prompt_cache_options", "required": false, "type": "object" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "text" ], "enum_known": true, "name": "modalities", "required": false, "type": "string[]" }, { "availability": "source_interface", "conditions": "此 Ultra 文字路由拒絕 provider.options", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "object" }, { "availability": "source_interface", "conditions": "實際提示詞快取位置拒絕 1h", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "cache_control.ttl", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "additional models/models list", "plugins", "route", "hosted tools; ordinary client function tools remain separate", "non-text output modalities", "provider.options overrides", "one-hour prompt-cache TTL" ] }, "hints": [ "將確切模型 ID 與 messages 一併送出。", "上下文長度不是輸出上限。", "文字檔案可提供原文文字；圖片／音訊／檔案物件輸入需要精確綁定。" ], "input": { "content_type": "application/json", "control_scope": "依模型而定；使用者函式工具與託管工具不同", "file_material": "實際 UTF-8 文件以原文文字插入；不推定檔案 ID", "hosted_operations": false, "material_types": [ "text" ], "messages": "具有 role/content 結構的訊息陣列", "multimodal_input_binding": null, "optional_controls": [ "stream", "temperature", "top_p", "stop", "max_tokens", "max_completion_tokens", "reasoning", "response_format", "tools", "tool_choice" ], "output_modalities": [ "text" ], "required": [ "model", "messages" ] }, "lifecycle": { "mode": "synchronous_or_streamed", "unknown": "preserve original request id; no generic chat job retrieval path" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [], "operation_modes": [ "text_chat" ], "output": { "content": "choices[].message.content or stream delta", "family": "text", "format": "json or supported SSE", "tool_calls": "retain actual returned function calls" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "詞元基本費率", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "dimensions": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "請求長度級距 1", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "declared_tariff_references": [ { "client_selectable": false, "key": "dd403f513dffcdc5a75d", "label": "google/gemini-3-pro-image-preview", "profiles": [ { "basis": "", "dimensions": { "conditions": "預設設定" }, "label": "google/gemini-3-pro-image-preview · 預設設定", "units": [ { "amount": "0.004", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "e6957656f069545cb1e5fe01", "label": "Input", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "tokens" }, { "amount": "0.24", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "2e51394f953e56115402542f", "label": "Output", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "tokens" } ] } ] }, { "client_selectable": false, "key": "3ba80d11c6a9d6a9a06e", "label": "Google: Nano Banana Pro (Gemini 3 Pro Image Preview)", "profiles": [ { "basis": "", "dimensions": { "conditions": "預設設定" }, "label": "Google: Nano Banana Pro (Gemini 3 Pro Image Preview) · 預設設定", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "6da94d9d3d98173f5e6e9c66", "label": "Input tokens", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "1a45fdb7264f6279403e9086", "label": "Output tokens", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "output_token" }, { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "bc57ce11b8664d047f995550", "label": "Input image", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_image_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "ad0457f9fab1294591cda73f", "label": "Output image", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "output_image_token" }, { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "8eb63c3adff16c514a8fb844", "label": "Audio input", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "input_audio_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "0b6ad5dff6255850f2934265", "label": "Audio cache read", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "cached_audio_input_token" }, { "amount": "0.028", "amount_unit": "usd_per_exact_physical_unit", "billing_input": false, "key": "5810c5c1ff81f13134623408", "label": "Web search", "public_unit_label": "per search", "quantity": "1", "rate_role": "declared_tariff_rate", "side": "out", "unit": "web_search" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "6d275a019972ae8656d9333a", "label": "Reasoning tokens", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "out", "unit": "internal_reasoning_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "92b31eda5cb5bd9931a89643", "label": "Cache read", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.000750000", "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "key": "461b53e0807f50a40039778e", "label": "Cache write · 5 minutes", "public_unit_label": "/ 1K tokens", "quantity": 1000, "rate_role": "declared_tariff_rate", "side": "in", "unit": "cache_write_token" } ] } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"google/gemini-3-pro-image-preview\", \"messages\": [{\"role\": \"user\", \"content\": \"Explain the difference between an observation and a hypothesis in three sentences.\"}]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model='google/gemini-3-pro-image-preview',messages=[{\"role\":\"user\",\"content\":\"Explain observations and hypotheses in three sentences.\"}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n", "label": "Python SDK · all choices", "language": "python" }, { "code": "from openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nwith client.chat.completions.stream(model='google/gemini-3-pro-image-preview',messages=[{\"role\":\"user\",\"content\":\"Explain observations and hypotheses in three sentences.\"}]) as stream:\n with Path(\"stream-events.jsonl\").open(\"w\",encoding=\"utf-8\") as log:\n for event in stream:\n payload=event.to_dict(mode=\"json\")\n log.write(json.dumps(payload,ensure_ascii=False)+\"\\n\"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode=\"json\"),\"stream-result\")\n", "label": "Python SDK streaming · all events", "language": "python" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "chat", "hints": [ "Send exact model ID with messages.", "Context length is not an output cap.", "A text file may supply literal text; image/audio/file-object inputs need exact binding." ], "http": "POST", "in": 4, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 24, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "快速模式", "value": "標準費率", "when": "選取快速模式時" }, { "label": "flex 模式", "value": "標準費率", "when": "選取 flex 模式時" }, { "label": "最高推理強度", "value": "標準費率", "when": "最高推理強度採用 default 調整值時" }, { "label": "顯式快取計價", "value": "未啟用", "when": "請求顯式快取時" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": 0.00024, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "dimensions": { "max_context_tokens_inclusive": null, "min_context_tokens_exclusive": 0 }, "label": "Request-length tier 1", "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "google/gemini-3-pro-image-preview", "route_label": "Ultra", "status_reads": [], "tiers": [ { "cache_read": 0.4, "cache_read_multiplier": null, "cache_write": 0.75, "cache_write_1h": null, "cache_write_multiplier": null, "created_at": "0001-01-01T00:00:00Z", "in": 4, "input_multiplier": null, "interval_boundary": "min_exclusive_max_inclusive", "max": null, "max_tokens": null, "min": 0, "min_tokens": 0, "null_rate_policy": "unknown or inherited; never zero", "out": 24, "output_multiplier": null, "per_request_price": null, "sort_order": 0, "tier_label": "", "updated_at": "0001-01-01T00:00:00Z" } ], "units": [ { "amount": "0.004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.000024", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "0.0004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0.0000004", "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": "0.00075000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0.00000075", "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.24000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "image_output_price", "label": "Image output tokens", "native_amount_usd_per_unit": "0.00024", "native_quantity": 1, "native_unit": "image_output_token", "public_unit_label": "/ 1K image tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "image_output_token" } ] }, { "available": null, "billing_basis": { "basis": "original_invoice_usd", "description": "顯示的費率用於在生成前估算費用。最終費用依原始生成作業記錄的成本計算。實測的輸出時間長度與預估數量並非最終計費數量。", "forecast_quantity_is_estimate": true, "physical_output_is_billable_quantity": false, "version": "original_invoice_usd_v1" }, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "actual returned task_id or media library record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "保留回傳的 X-Client-Request-ID，以供核對。僅讀取實際回傳的 task_id/asset_id 值或自己擁有的媒體庫；請勿以作業 ID 組成任務 URL，也請勿重新提交建立請求。" }, "outputs": { "all_results": "保留完整回應與標頭、所有實際輸出、原始回傳的識別碼，以及所有用量欄位。恢復原始作業時，請勿再次提交建立請求。", "family": "image", "fields": [ { "description": null, "name": "created", "type": null }, { "description": null, "name": "data[].url", "type": null }, { "description": null, "name": "data[].b64_json", "type": null }, { "description": null, "name": "usage", "type": null } ] }, "parameters": [ { "enum": [ "google/gemini-3-pro-image-preview" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "min_length": 1, "name": "prompt", "required": true, "type": "string" }, { "enum": [ "1K", "2K", "4K" ], "enum_known": true, "name": "resolution", "required": false, "type": "string" }, { "enum": [ "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9" ], "enum_known": true, "name": "aspect_ratio", "required": false, "type": "string" }, { "maximum": 1, "minimum": 1, "name": "n", "required": false, "type": "integer" }, { "items": { "properties": { "image_url": { "properties": { "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": { "const": "image_url" } }, "required": [ "type", "image_url" ], "type": "object" }, "maximum_items": 14, "minimum_items": 0, "name": "input_references", "required": false, "type": "array" }, { "name": "provider", "required": false, "type": "object" } ], "request_schema": { "properties": { "aspect_ratio": { "enum": [ "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9" ], "type": "string" }, "input_references": { "items": { "properties": { "image_url": { "properties": { "url": { "type": "string" } }, "required": [ "url" ], "type": "object" }, "type": { "const": "image_url" } }, "required": [ "type", "image_url" ], "type": "object" }, "maxItems": 14, "minItems": 0, "type": "array" }, "model": { "enum": [ "google/gemini-3-pro-image-preview" ], "type": "string" }, "n": { "maximum": 1, "minimum": 1, "type": "integer" }, "prompt": { "minLength": 1, "type": "string" }, "provider": { "description": "Exact image provider preferences from the documented image request schema.", "type": "object" }, "resolution": { "enum": [ "1K", "2K", "4K" ], "type": "string" } }, "required": [ "model", "prompt" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": false, "body": { "model": "google/gemini-3-pro-image-preview", "prompt": "a red panda astronaut floating in space, studio lighting" }, "content_type": "application/json" } }, "input": { "content_type": "application/json", "material_types": [ "text", "image" ], "required": [ "model", "prompt" ] }, "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed URL; never construct token" } ], "mode": "synchronous_result_then_owned_library", "operation_id_is_not_task_id": true, "status": [ { "http": "GET", "id_source": "實際回傳的 task_id 或媒體庫紀錄", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation." }, "method": { "http": "POST", "path": "/v1/images/generations" }, "output": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "family": "image", "fields": [ { "description": null, "name": "created", "type": null }, { "description": null, "name": "data[].url", "type": null }, { "description": null, "name": "data[].b64_json", "type": null }, { "description": null, "name": "usage", "type": null } ] } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": false, "estimate_only": true, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "basis": "此估算並非額外費用，也不是本次生成完成後的固定費率。", "dimensions": { "forecast_quantity": "one generation estimate" }, "label": "預估生成費用", "units": [ { "amount": "0.98304", "amount_unit": "usd_per_exact_native_unit", "billing_input": false, "estimate_only": true, "key": "forecast_usd", "label": "Estimated generation cost", "native_amount_usd_per_unit": "0.98304", "native_quantity": 1, "public_unit_label": "estimate before generation", "quantity": 1, "quantity_is_estimate": true, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "request_estimate" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'google/gemini-3-pro-image-preview', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit(\"No completed images; retain the full response\")\n for index, item in enumerate(items):\n encoded=item.get(\"b64_json\")\n uri=item.get(\"url\", \"\")\n if not encoded and uri.startswith(\"data:image/\"):\n header, separator, encoded=uri.partition(\",\")\n if not separator or \";base64\" not in header: raise SystemExit(\"Unsupported image data URI\")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin(\"https://api.ailoot.ai\",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!=\"https\" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit(\"Invalid returned image URL\")\n owned=parsed.netloc==\"api.ailoot.ai\" and parsed.path.startswith(\"/v1/media/assets/\") and parsed.path.endswith(\"/content\")\n args=[\"curl\",\"--fail-with-body\",\"--silent\",\"--show-error\",uri]\n if owned: args += [\"-H\",\"Authorization: Bearer \"+os.environ[\"AILOOT_API_KEY\"]]\n temporary=Path(f\"image-{index}.bin\")\n subprocess.run(args+[\"-o\",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit(\"Image has neither bytes nor URL\")\n if not raw: raise SystemExit(\"Empty image result\")\n ext=\"png\" if raw.startswith(b\"\\x89PNG\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4]==b\"RIFF\" and raw[8:12]==b\"WEBP\" else \"svg\" if raw.lstrip().startswith((b\"