Gemini 3.5 Flash is Google’s multimodal Flash model profile for sustained coding and agentic work. Its documented inputs are text, images, video, audio and PDF, with text output. The model’s task focus includes iterative coding, exploration of alternate solutions and longer sequences of instructions and tool use. The 3.5 edition provides documented thinking controls, multimodal inputs and tool workflows.
A coding task can combine a written requirement, relevant source files and a screenshot of the intended result. Supply the observed behaviour, the expected behaviour and the constraints that must stay intact. An illustrative brief is: “Compare the screenshot with the component text, identify the mismatch in the form workflow and propose one focused correction. Explain which source details justify the change.” For a document task, provide the relevant pages and ask for a comparison against explicit requirements, with source references for each finding.
The edition documents minimal, low, medium and high thinking levels. Medium is the default, changed from the high default of Gemini 3 Flash Preview. Minimal suits simple answers and straightforward tool decisions without guaranteeing that reasoning is completely disabled; low targets tasks requiring fewer steps; high provides deeper reasoning for hard mathematics, coding and agent problems. Google recommends using thinking_level for effort control and retaining default sampling settings rather than trying to force reasoning by changing temperature, top-p or top-k.
A defining behaviour is preservation of reasoning context across a multi-turn task. Google’s Interactions workflow carries that context automatically; its GenerateContent workflow requires the full unmodified conversation history, including thought signatures. This supports a continuing debugging or refactoring assignment in which earlier reasoning informs the next step. Preserve the actual conversation structure rather than converting each follow-up into an unrelated prompt, and keep the final requested artifact clear as the work progresses.
The documented tool set includes Google Search, grounding with Google Maps, File Search, Code Execution, URL Context and standard Function Calling, with combined tool use and Computer Use marked as a preview capability. Structured Outputs can supply a defined data record. Give functions a clear purpose and parameter definition: the model chooses a function, while the connected application performs its operation and returns the result. Google requires function results to match the preceding calls by identity, name and count; multimodal results belong inside the function response.
The exact API model table gives a maximum input of 1,048,576 tokens and maximum output of 65,536 tokens. Conversation history and supplied media contribute to the input budget. Caching and batch processing are documented for this profile, with additional Flex and Priority processing options in Google’s table. These limits and capabilities describe the producer edition; they do not require an answer to fill the entire output window or make every tool necessary for a task.
This profile generates text rather than images or audio and does not use Live API. Its developer guide also excludes image segmentation. For a visual question, ask for a description, extraction or interpretation of the supplied material; for a spoken recording, ask for a text result. When the answer depends on newer information, the documented search-grounding workflow can add sources beyond training knowledge. State whether external information is needed and what evidence the final answer should retain.
For a useful longer assignment, define the final result and the completion criterion before the first step. A support-analysis task might request a table of observed issues, supporting passages and a proposed reply; a coding task might require the focused change and the relevant validation result. Attach the source context that matters, specify the permitted tool actions and request an explicit final answer that separates completed work from questions still requiring information. This turns the model’s sustained workflow into a concrete text, code or structured artifact.
", "aboutText": "Gemini 3.5 Flash is Google’s multimodal Flash model profile for sustained coding and agentic work. Its documented inputs are text, images, video, audio and PDF, with text output. The model’s task focus includes iterative coding, exploration of alternate solutions and longer sequences of instructions and tool use. The 3.5 edition provides documented thinking controls, multimodal inputs and tool workflows.\n\nA coding task can combine a written requirement, relevant source files and a screenshot of the intended result. Supply the observed behaviour, the expected behaviour and the constraints that must stay intact. An illustrative brief is: “Compare the screenshot with the component text, identify the mismatch in the form workflow and propose one focused correction. Explain which source details justify the change.” For a document task, provide the relevant pages and ask for a comparison against explicit requirements, with source references for each finding.\n\nThe edition documents minimal, low, medium and high thinking levels. Medium is the default, changed from the high default of Gemini 3 Flash Preview. Minimal suits simple answers and straightforward tool decisions without guaranteeing that reasoning is completely disabled; low targets tasks requiring fewer steps; high provides deeper reasoning for hard mathematics, coding and agent problems. Google recommends using thinking_level for effort control and retaining default sampling settings rather than trying to force reasoning by changing temperature, top-p or top-k.\n\nA defining behaviour is preservation of reasoning context across a multi-turn task. Google’s Interactions workflow carries that context automatically; its GenerateContent workflow requires the full unmodified conversation history, including thought signatures. This supports a continuing debugging or refactoring assignment in which earlier reasoning informs the next step. Preserve the actual conversation structure rather than converting each follow-up into an unrelated prompt, and keep the final requested artifact clear as the work progresses.\n\nThe documented tool set includes Google Search, grounding with Google Maps, File Search, Code Execution, URL Context and standard Function Calling, with combined tool use and Computer Use marked as a preview capability. Structured Outputs can supply a defined data record. Give functions a clear purpose and parameter definition: the model chooses a function, while the connected application performs its operation and returns the result. Google requires function results to match the preceding calls by identity, name and count; multimodal results belong inside the function response.\n\nThe exact API model table gives a maximum input of 1,048,576 tokens and maximum output of 65,536 tokens. Conversation history and supplied media contribute to the input budget. Caching and batch processing are documented for this profile, with additional Flex and Priority processing options in Google’s table. These limits and capabilities describe the producer edition; they do not require an answer to fill the entire output window or make every tool necessary for a task.\n\nThis profile generates text rather than images or audio and does not use Live API. Its developer guide also excludes image segmentation. For a visual question, ask for a description, extraction or interpretation of the supplied material; for a spoken recording, ask for a text result. When the answer depends on newer information, the documented search-grounding workflow can add sources beyond training knowledge. State whether external information is needed and what evidence the final answer should retain.\n\nFor a useful longer assignment, define the final result and the completion criterion before the first step. A support-analysis task might request a table of observed issues, supporting passages and a proposed reply; a coding task might require the focused change and the relevant validation result. Attach the source context that matters, specify the permitted tool actions and request an explicit final answer that separates completed work from questions still requiring information. This turns the model’s sustained workflow into a concrete text, code or structured artifact.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "f6b8b920456465c5d63f2930cc0203832da14621e3a086c35141ed23e4a639d1", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "gemini-3.5-flash-thinkingGemini 3.5 Flash combines multimodal understanding with reasoning for coding and multi-step agent tasks. It accepts text, images, video, audio and PDF and produces text, with structured outputs, tools and a 1,048,576-token input limit in its documented profile.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemini 3.5 Flash is Google’s multimodal Flash model profile for sustained coding and agentic work. Its documented inputs are text, images, video, audio and PDF, with text output. The model’s task focus includes iterative coding, exploration of alternate solutions and longer sequences of instructions and tool use. The 3.5 edition provides documented thinking controls, multimodal inputs and tool workflows.
A coding task can combine a written requirement, relevant source files and a screenshot of the intended result. Supply the observed behaviour, the expected behaviour and the constraints that must stay intact. An illustrative brief is: “Compare the screenshot with the component text, identify the mismatch in the form workflow and propose one focused correction. Explain which source details justify the change.” For a document task, provide the relevant pages and ask for a comparison against explicit requirements, with source references for each finding.
The edition documents minimal, low, medium and high thinking levels. Medium is the default, changed from the high default of Gemini 3 Flash Preview. Minimal suits simple answers and straightforward tool decisions without guaranteeing that reasoning is completely disabled; low targets tasks requiring fewer steps; high provides deeper reasoning for hard mathematics, coding and agent problems. Google recommends using thinking_level for effort control and retaining default sampling settings rather than trying to force reasoning by changing temperature, top-p or top-k.
A defining behaviour is preservation of reasoning context across a multi-turn task. Google’s Interactions workflow carries that context automatically; its GenerateContent workflow requires the full unmodified conversation history, including thought signatures. This supports a continuing debugging or refactoring assignment in which earlier reasoning informs the next step. Preserve the actual conversation structure rather than converting each follow-up into an unrelated prompt, and keep the final requested artifact clear as the work progresses.
The documented tool set includes Google Search, grounding with Google Maps, File Search, Code Execution, URL Context and standard Function Calling, with combined tool use and Computer Use marked as a preview capability. Structured Outputs can supply a defined data record. Give functions a clear purpose and parameter definition: the model chooses a function, while the connected application performs its operation and returns the result. Google requires function results to match the preceding calls by identity, name and count; multimodal results belong inside the function response.
The exact API model table gives a maximum input of 1,048,576 tokens and maximum output of 65,536 tokens. Conversation history and supplied media contribute to the input budget. Caching and batch processing are documented for this profile, with additional Flex and Priority processing options in Google’s table. These limits and capabilities describe the producer edition; they do not require an answer to fill the entire output window or make every tool necessary for a task.
This profile generates text rather than images or audio and does not use Live API. Its developer guide also excludes image segmentation. For a visual question, ask for a description, extraction or interpretation of the supplied material; for a spoken recording, ask for a text result. When the answer depends on newer information, the documented search-grounding workflow can add sources beyond training knowledge. State whether external information is needed and what evidence the final answer should retain.
For a useful longer assignment, define the final result and the completion criterion before the first step. A support-analysis task might request a table of observed issues, supporting passages and a proposed reply; a coding task might require the focused change and the relevant validation result. Attach the source context that matters, specify the permitted tool actions and request an explicit final answer that separates completed work from questions still requiring information. This turns the model’s sustained workflow into a concrete text, code or structured artifact.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gemini-3.5-flash-thinking | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Model-specific | Default— |
Fieldmessages[].content | Typestring | RequirementOptional | Values & limits Applies when: Text conversation | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream |
Parametercontrol_scope | ContractText conversation; stream is a boolean and defaults to false. |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Method aliases: /chat/completions
Lifecycle: synchronous_or_streamed
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.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.003 / 1K tokens Output · Output tokens $0.018 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model":"gemini-3.5-flash-thinking","messages":[{"role":"user","content":"hello"}],"stream":false}')\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\nfrom 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="gemini-3.5-flash-thinking", messages=[{"role":"user","content":"hello"}], stream=False)\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"))\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_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 Ultra gemini-3.5-flash-thinking
/chat/completions
gemini-3.5-flash-thinkingGemini 3.5 Flash combines multimodal understanding with reasoning for coding and multi-step agent tasks. It accepts text, images, video, audio and PDF and produces text, with structured outputs, tools and a 1,048,576-token input limit in its documented profile.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemini 3.5 Flash is Google’s multimodal Flash model profile for sustained coding and agentic work. Its documented inputs are text, images, video, audio and PDF, with text output. The model’s task focus includes iterative coding, exploration of alternate solutions and longer sequences of instructions and tool use. The 3.5 edition provides documented thinking controls, multimodal inputs and tool workflows.
A coding task can combine a written requirement, relevant source files and a screenshot of the intended result. Supply the observed behaviour, the expected behaviour and the constraints that must stay intact. An illustrative brief is: “Compare the screenshot with the component text, identify the mismatch in the form workflow and propose one focused correction. Explain which source details justify the change.” For a document task, provide the relevant pages and ask for a comparison against explicit requirements, with source references for each finding.
The edition documents minimal, low, medium and high thinking levels. Medium is the default, changed from the high default of Gemini 3 Flash Preview. Minimal suits simple answers and straightforward tool decisions without guaranteeing that reasoning is completely disabled; low targets tasks requiring fewer steps; high provides deeper reasoning for hard mathematics, coding and agent problems. Google recommends using thinking_level for effort control and retaining default sampling settings rather than trying to force reasoning by changing temperature, top-p or top-k.
A defining behaviour is preservation of reasoning context across a multi-turn task. Google’s Interactions workflow carries that context automatically; its GenerateContent workflow requires the full unmodified conversation history, including thought signatures. This supports a continuing debugging or refactoring assignment in which earlier reasoning informs the next step. Preserve the actual conversation structure rather than converting each follow-up into an unrelated prompt, and keep the final requested artifact clear as the work progresses.
The documented tool set includes Google Search, grounding with Google Maps, File Search, Code Execution, URL Context and standard Function Calling, with combined tool use and Computer Use marked as a preview capability. Structured Outputs can supply a defined data record. Give functions a clear purpose and parameter definition: the model chooses a function, while the connected application performs its operation and returns the result. Google requires function results to match the preceding calls by identity, name and count; multimodal results belong inside the function response.
The exact API model table gives a maximum input of 1,048,576 tokens and maximum output of 65,536 tokens. Conversation history and supplied media contribute to the input budget. Caching and batch processing are documented for this profile, with additional Flex and Priority processing options in Google’s table. These limits and capabilities describe the producer edition; they do not require an answer to fill the entire output window or make every tool necessary for a task.
This profile generates text rather than images or audio and does not use Live API. Its developer guide also excludes image segmentation. For a visual question, ask for a description, extraction or interpretation of the supplied material; for a spoken recording, ask for a text result. When the answer depends on newer information, the documented search-grounding workflow can add sources beyond training knowledge. State whether external information is needed and what evidence the final answer should retain.
For a useful longer assignment, define the final result and the completion criterion before the first step. A support-analysis task might request a table of observed issues, supporting passages and a proposed reply; a coding task might require the focused change and the relevant validation result. Attach the source context that matters, specify the permitted tool actions and request an explicit final answer that separates completed work from questions still requiring information. This turns the model’s sustained workflow into a concrete text, code or structured artifact.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gemini-3.5-flash-thinking | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.003 / 1K tokens Output · Output tokens $0.018 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "gemini-3.5-flash-thinking",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'gemini-3.5-flash-thinking', '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\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': 'gemini-3.5-flash-thinking', '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))\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 Ultra gemini-3.5-flash-thinking
/chat/completions