Gemini 3.1 Pro Preview is intended for complex material and tasks requiring several sequential decisions. Google highlights software engineering, tool use and consistency of factual answers. The model helps analyze large datasets, entire software repositories and tasks combining text with visual or audio material.
Google's specifications list text, images, video, audio and PDF. In Ailoot API's current catalogue, text methods declare text and images as input and text as output. Video, audio and PDF are not included in this input set. Long context holds up to 1,048,576 input tokens, and a text response up to 65,536 output tokens. Understanding images and audio does not mean generating them: this version's output remains text.
Thinking allows additional effort to be devoted to internal reasoning. In Google's described base-model mode, the default level is high, with low, medium and high available. These values describe Google's base model; the mode and availability of control through Ailoot API depend on the selected method and model variant. Main uses include complex coding, algorithm development, strategic planning and iterative improvement of a solution. This is especially useful when several connected requirements need to be considered and intermediate results revisited.
The model's capabilities include function calling, code execution and structured responses. Context caching, search grounding, Google Maps and use of URL contents are supported; batch processing is provided. This set allows a workflow to be built in which the model analyzes data, selects the next tool and forms a final result. Actual availability of each tool depends on the method used to work with the model.
The card concerns the gemini-3.1-pro-preview preview version. It is based on Gemini 3 Pro. The manufacturer separately identifies gemini-3.1-pro-preview-customtools, intended for workflows involving a command shell and custom tools. Its tool-selection priorities and behavioral characteristics are not properties of the ordinary Preview version.
The general limitations of the base line remain: responses may contain fabricated information, and some requests may run slowly or time out. Multi-step reasoning, large context and data access help with complex tasks, but do not make every generated conclusion a verified fact.
", "aboutText": "Gemini 3.1 Pro Preview is intended for complex material and tasks requiring several sequential decisions. Google highlights software engineering, tool use and consistency of factual answers. The model helps analyze large datasets, entire software repositories and tasks combining text with visual or audio material.\n\n### Material the model understands\n\nGoogle's specifications list **text, images, video, audio and PDF**. In Ailoot API's current catalogue, text methods declare **text and images** as input and text as output. Video, audio and PDF are not included in this input set. Long context holds up to **1,048,576 input tokens**, and a text response up to **65,536 output tokens**. Understanding images and audio does not mean generating them: this version's output remains text.\n\n### Reasoning and working step by step\n\nThinking allows additional effort to be devoted to internal reasoning. In Google's described base-model mode, the default level is `high`, with `low`, `medium` and `high` available. These values describe Google's base model; the mode and availability of control through Ailoot API depend on the selected method and model variant. Main uses include complex coding, algorithm development, strategic planning and iterative improvement of a solution. This is especially useful when several connected requirements need to be considered and intermediate results revisited.\n\nThe model's capabilities include function calling, code execution and structured responses. Context caching, search grounding, Google Maps and use of URL contents are supported; batch processing is provided. This set allows a workflow to be built in which the model analyzes data, selects the next tool and forms a final result. Actual availability of each tool depends on the method used to work with the model.\n\n### Version and characteristics\n\nThe card concerns the `gemini-3.1-pro-preview` preview version. It is based on Gemini 3 Pro. The manufacturer separately identifies `gemini-3.1-pro-preview-customtools`, intended for workflows involving a command shell and custom tools. Its tool-selection priorities and behavioral characteristics are not properties of the ordinary Preview version.\n\nThe general limitations of the base line remain: responses may contain fabricated information, and some requests may run slowly or time out. Multi-step reasoning, large context and data access help with complex tasks, but do not make every generated conclusion a verified fact.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Design a dependency-aware work order\n\nPlan execution for these tasks: A has no dependency; B depends on A; C depends on A; D depends on both B and C. At most two tasks may run together, and each takes one hour. Give the earliest valid schedule, a general scheduling algorithm and a test that catches starting D before C finishes.A time-slot schedule, algorithm pseudocode and a dependency-violation test case.Check that the proposed schedule respects both dependencies and the two-task concurrency limit.gemini-pro-agentA multimodal Google model for complex coding tasks, analysis of large bodies of material and precise multi-step tool use.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemini 3.1 Pro Preview is intended for complex material and tasks requiring several sequential decisions. Google highlights software engineering, tool use and consistency of factual answers. The model helps analyze large datasets, entire software repositories and tasks combining text with visual or audio material.
Google's specifications list text, images, video, audio and PDF. In Ailoot API's current catalogue, text methods declare text and images as input and text as output. Video, audio and PDF are not included in this input set. Long context holds up to 1,048,576 input tokens, and a text response up to 65,536 output tokens. Understanding images and audio does not mean generating them: this version's output remains text.
Thinking allows additional effort to be devoted to internal reasoning. In Google's described base-model mode, the default level is high, with low, medium and high available. These values describe Google's base model; the mode and availability of control through Ailoot API depend on the selected method and model variant. Main uses include complex coding, algorithm development, strategic planning and iterative improvement of a solution. This is especially useful when several connected requirements need to be considered and intermediate results revisited.
The model's capabilities include function calling, code execution and structured responses. Context caching, search grounding, Google Maps and use of URL contents are supported; batch processing is provided. This set allows a workflow to be built in which the model analyzes data, selects the next tool and forms a final result. Actual availability of each tool depends on the method used to work with the model.
The card concerns the gemini-3.1-pro-preview preview version. It is based on Gemini 3 Pro. The manufacturer separately identifies gemini-3.1-pro-preview-customtools, intended for workflows involving a command shell and custom tools. Its tool-selection priorities and behavioral characteristics are not properties of the ordinary Preview version.
The general limitations of the base line remain: responses may contain fabricated information, and some requests may run slowly or time out. Multi-step reasoning, large context and data access help with complex tasks, but do not make every generated conclusion a verified fact.
POST /v1/messagestext, imagetext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gemini-pro-agent | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring|content_block[] | RequirementRequired | Values & limits | Default— |
Fieldsystem | Typestring|content_block[] | RequirementOptional | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits Minimum: 1 Limit applies to: exact selected model/card ceiling | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop_sequences | Typestring[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typetool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].name | Typestring | RequirementOptional | Values & limits Applies when: tool supplied | Default— |
Fieldtools[].description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].input_schema | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldthinking.type | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldthinking.budget_tokens | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_config.effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmetadata | Typeobject | RequirementOptional | Values & limits | Default— |
Fieldcache_control | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Text · UTF8 text
messages[].content or type=text block
Text + image
{type:image,source:{type:base64,media_type,data}} or source type=url
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext, image |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Parametertext_file_material | Contractactual UTF-8 content inserted as text; file IDs/URLs need separate exact support |
Lifecycle: exact retained published method
Method shapes and IDs are exact; supplier capability is separate from admission.
POST /v1/messages| Request size, tokens | Input | Output | Cache read | Cache write | 5 min cache | 1 hour cache |
|---|---|---|---|---|---|---|
| Request size, tokens(0; 200,000] | Input$0.002 | Output$0.012 | Cache read$0.0002 | Cache writeBase: — | 5 min cacheBase: — | 1 hour cacheBase: — |
| Request size, tokens(200,000; ∞) | Input$0.004 | Output$0.018 | Cache read$0.0004 | Cache writeBase: — | 5 min cacheBase: — | 1 hour cacheBase: — |
Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set.
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gemini-pro-agent", "max_tokens": 1024, "messages": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\\n\\n"+text}]\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/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gemini-pro-agent", "max_tokens": 1024, "messages": [{"role": "user", "content": [{"type": "text", "text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["messages"][0]["content"].append({"type":"image","source":{"type":"base64","media_type":mime,"data":b64}})\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/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\nprint(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\nimport anthropic\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)\nmsg = client.messages.create(\n model="gemini-pro-agent",\n max_tokens=1024,\n messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],\n)\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(msg.to_dict(mode="json"))\nimport Anthropic from "@anthropic-ai/sdk";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });\nconst msg = await client.messages.create({\n model: "gemini-pro-agent",\n max_tokens: 1024,\n messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],\n});\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(msg);\nimport anthropic,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=anthropic.Anthropic(base_url="https://api.ailoot.ai",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.messages.stream(model='gemini-pro-agent',max_tokens=1024,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_message()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
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.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot gemini-pro-agent
Use the method paths shown above.
gemini-pro-agentA multimodal Google model for complex coding tasks, analysis of large bodies of material and precise multi-step tool use.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemini 3.1 Pro Preview is intended for complex material and tasks requiring several sequential decisions. Google highlights software engineering, tool use and consistency of factual answers. The model helps analyze large datasets, entire software repositories and tasks combining text with visual or audio material.
Google's specifications list text, images, video, audio and PDF. In Ailoot API's current catalogue, text methods declare text and images as input and text as output. Video, audio and PDF are not included in this input set. Long context holds up to 1,048,576 input tokens, and a text response up to 65,536 output tokens. Understanding images and audio does not mean generating them: this version's output remains text.
Thinking allows additional effort to be devoted to internal reasoning. In Google's described base-model mode, the default level is high, with low, medium and high available. These values describe Google's base model; the mode and availability of control through Ailoot API depend on the selected method and model variant. Main uses include complex coding, algorithm development, strategic planning and iterative improvement of a solution. This is especially useful when several connected requirements need to be considered and intermediate results revisited.
The model's capabilities include function calling, code execution and structured responses. Context caching, search grounding, Google Maps and use of URL contents are supported; batch processing is provided. This set allows a workflow to be built in which the model analyzes data, selects the next tool and forms a final result. Actual availability of each tool depends on the method used to work with the model.
The card concerns the gemini-3.1-pro-preview preview version. It is based on Gemini 3 Pro. The manufacturer separately identifies gemini-3.1-pro-preview-customtools, intended for workflows involving a command shell and custom tools. Its tool-selection priorities and behavioral characteristics are not properties of the ordinary Preview version.
The general limitations of the base line remain: responses may contain fabricated information, and some requests may run slowly or time out. Multi-step reasoning, large context and data access help with complex tasks, but do not make every generated conclusion a verified fact.
POST /v1beta/models/{model}:generateContenttext, imagetext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldpath.model | Typestring | RequirementRequired | Values & limits gemini-pro-agent | Default— |
Fieldcontents | Typecontent[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldcontents[].role | Typestring | RequirementOptional | Values & limits user, model | Default— |
Fieldcontents[].parts | Typepart[] | RequirementRequired | Values & limits | Default— |
FieldsystemInstruction | Typecontent | RequirementOptional | Values & limits | Default— |
FieldgenerationConfig.maxOutputTokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
FieldgenerationConfig.temperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
FieldgenerationConfig.topP | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
FieldgenerationConfig.topK | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
FieldgenerationConfig.stopSequences | Typestring[] | RequirementOptional | Values & limits Model-specific | Default— |
FieldgenerationConfig.thinkingConfig.includeThoughts | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
FieldgenerationConfig.thinkingConfig.thinkingBudget | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].functionDeclarations | Typefunction_declaration[] | RequirementOptional | Values & limits Model-specific | Default— |
FieldtoolConfig.functionCallingConfig.mode | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
FieldsafetySettings | Typeobject[] | RequirementOptional | Values & limits Model-specific | Default— |
FieldsessionId | Typestring | RequirementOptional | Values & limits | Default— |
Text · UTF8 text
contents[].parts[].text
Text + image
parts[].inlineData={mimeType,data}
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext, image |
Parameterrequired | Contractcontents |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
Parametertext_file_material | Contractactual UTF-8 content inserted as text; file IDs/URLs need separate exact support |
Lifecycle: exact retained published method
Method shapes and IDs are exact; supplier capability is separate from admission.
POST /v1beta/models/{model}:generateContent| Request size, tokens | Input | Output | Cache read | Cache write | 5 min cache | 1 hour cache |
|---|---|---|---|---|---|---|
| Request size, tokens(0; 200,000] | Input$0.002 | Output$0.012 | Cache read$0.0002 | Cache writeBase: — | 5 min cacheBase: — | 1 hour cacheBase: — |
| Request size, tokens(200,000; ∞) | Input$0.004 | Output$0.018 | Cache read$0.0004 | Cache writeBase: — | 5 min cacheBase: — | 1 hour cacheBase: — |
Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set.
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"contents": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["contents"]=[{"role":"user","parts":[{"text":"Summarize this material and quote supporting source passages.\\n\\n"+text}]}]\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1beta/models/gemini-pro-agent:generateContent \\\n -H "x-goog-api-key: $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\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(json.loads(Path("result.json").read_text(encoding="utf-8")))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"\nexport AILOOT_IMAGE_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"contents": [{"role": "user", "parts": [{"text": "Read labels and numbers in this image; flag unreadable text."}]}]}')\nimport base64\nf=Path(os.environ["AILOOT_IMAGE_FILE"])\nif not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")\nraw=f.read_bytes()\nmime="image/png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "image/jpeg" if raw.startswith(b"\\xff\\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None\nif not mime:raise SystemExit("Use PNG JPEG or WebP")\nb64=base64.b64encode(raw).decode()\np["contents"][0]["parts"].append({"inlineData":{"mimeType":mime,"data":b64}})\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1beta/models/gemini-pro-agent:generateContent \\\n -H "x-goog-api-key: $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\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(json.loads(Path("result.json").read_text(encoding="utf-8")))\nPY_RESULT\nfrom google import genai\nfrom google.genai import types\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient = genai.Client(\n api_key=os.environ["AILOOT_API_KEY"],\n http_options=types.HttpOptions(base_url="https://api.ailoot.ai"),\n)\nresp = client.models.generate_content(model="gemini-pro-agent", contents="Explain the difference between an observation and a hypothesis 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(resp.to_json_dict())\nimport { GoogleGenAI } from "@google/genai";\n\nif (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");\nconst ai = new GoogleGenAI({ apiKey: process.env.AILOOT_API_KEY, httpOptions: { baseUrl: "https://api.ailoot.ai" } });\nconst resp = await ai.models.generateContent({ model: "gemini-pro-agent", contents: "Explain the difference between an observation and a hypothesis in three sentences." });\nimport { writeFileSync } from "node:fs";\n\nfunction saveResult(data, stem = "result") {\n writeFileSync(stem + ".json", JSON.stringify(data, null, 2));\n console.log(JSON.stringify(data, null, 2));\n const saveBlob = (encoded, suffix, label) => {\n const raw = Buffer.from(encoded, "base64");\n const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;\n const file = `${stem}-${label}.${ext}`;\n writeFileSync(file, raw); console.log("Saved", file);\n };\n for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));\n for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));\n for (const [i, item] of (data.output || []).entries()) {\n console.log("Output", i, JSON.stringify(item));\n if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);\n }\n for (const [ci, candidate] of (data.candidates || []).entries()) {\n for (const [pi, part] of (candidate.content?.parts || []).entries()) {\n console.log("Candidate/part", ci, pi, JSON.stringify(part));\n const blob = part.inlineData || part.inline_data;\n if (blob?.data) {\n const mime = blob.mimeType || blob.mime_type;\n const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";\n saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);\n }\n }\n }\n}\n\nsaveResult(resp);\nfrom google import genai\nfrom google.genai import types\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=genai.Client(api_key=os.environ["AILOOT_API_KEY"],http_options=types.HttpOptions(base_url="https://api.ailoot.ai"))\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\ncount=0\nwith Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for chunk in client.models.generate_content_stream(model='gemini-pro-agent',contents="Explain observations and hypotheses in three sentences."):\n payload=chunk.to_json_dict()\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n save_result(payload,"stream-chunk-"+str(count))\n count+=1\njson or supported streaming
Keep every complete returned response, file and content part.
Keep all candidates and parts, including function calls, thought signatures and usage; assemble stream chunks in order.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
FieldresponseId | Typestring |
FieldmodelVersion | Typestring |
Fieldcandidates[].content.parts[] | Typetext|inlineData|functionCall|other part |
Fieldcandidates[].finishReason | Typestring |
FieldusageMetadata | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot gemini-pro-agent
Use the method paths shown above.
Technical example in English
Design a dependency-aware work order\n\nPlan execution for these tasks: A has no dependency; B depends on A; C depends on A; D depends on both B and C. At most two tasks may run together, and each takes one hour. Give the earliest valid schedule, a general scheduling algorithm and a test that catches starting D before C finishes.A time-slot schedule, algorithm pseudocode and a dependency-violation test case.Check that the proposed schedule respects both dependencies and the two-task concurrency limit.