Mistral Medium is Mistral AI’s family for application assistants, document work and software agents. The Medium 3.5 edition combines instruction-following, reasoning and coding in a dense model with 128 billion parameters and a 256K context window. It understands text and visual material and produces text, code or structured results. Its vision encoder was trained to handle variable image sizes and aspect ratios, allowing a task to combine written requirements with screenshots, document pages or other visual evidence.
The model’s features begin with structured output and function calling. JSON output is useful when the response must preserve a machine-readable structure; a custom schema defines required fields and their types. A document task can return a table of extracted facts, a comparison of sections or a summary with open questions. Document question answering combines extraction of the document’s text and structure with language-model understanding, so the question can be related to the relevant content rather than a free-form guess.
In a tool-assisted task, the application describes its functions and the arguments they accept. The model chooses a function, prepares arguments and receives the result after the application executes the action. The conversation can continue with another tool call or a final answer. Parallel tool use lets independent calls be requested together where the application permits it. Tool-choice controls can allow the model to decide, require a tool or keep the task within a text response.
Medium 3.5 adjusts reasoning effort per request. A minimal-thinking setting returns the answer directly; high effort includes a reasoning segment before the final answer and is recommended for agentic and coding tasks. In a multi-turn reasoning conversation, complete assistant messages retain the information needed for follow-up work. Predicted outputs help when revising code or a document with known passages, and a supplied prefix can fix the opening of the answer. Batch workflows can process a collection of independent requests asynchronously.
The edition is designed for work that continues over many steps. Coding examples include module refactoring, bug fixing, generating checks, updating dependencies and investigating failed development workflows. A useful assignment supplies the affected files, the required behavior and a clear completion criterion. The model can use the results of repository exploration and application-run checks to revise its proposed solution; tools and file access are controlled by the surrounding agent environment.
For knowledge work, the manufacturer describes research, synthesis and workflows across connected tools. An Ailoot API task can ask for a structured brief assembled from supplied documents, a comparison of technical options or a draft based on retrieved information. Specify which observations support the conclusion and what artifact should complete the task. The model prepares the reasoning and result; the application supplies information access, performs actions and presents the deliverable. The Medium family name remains an updated selection, while the numerical architecture, visual processing and reasoning modes described here belong to the explicitly named Medium 3.5 edition.
", "aboutText": "Mistral Medium is Mistral AI’s family for application assistants, document work and software agents. The Medium 3.5 edition combines instruction-following, reasoning and coding in a dense model with 128 billion parameters and a 256K context window. It understands text and visual material and produces text, code or structured results. Its vision encoder was trained to handle variable image sizes and aspect ratios, allowing a task to combine written requirements with screenshots, document pages or other visual evidence.\n\nThe model’s features begin with structured output and function calling. JSON output is useful when the response must preserve a machine-readable structure; a custom schema defines required fields and their types. A document task can return a table of extracted facts, a comparison of sections or a summary with open questions. Document question answering combines extraction of the document’s text and structure with language-model understanding, so the question can be related to the relevant content rather than a free-form guess.\n\nIn a tool-assisted task, the application describes its functions and the arguments they accept. The model chooses a function, prepares arguments and receives the result after the application executes the action. The conversation can continue with another tool call or a final answer. Parallel tool use lets independent calls be requested together where the application permits it. Tool-choice controls can allow the model to decide, require a tool or keep the task within a text response.\n\nMedium 3.5 adjusts reasoning effort per request. A minimal-thinking setting returns the answer directly; high effort includes a reasoning segment before the final answer and is recommended for agentic and coding tasks. In a multi-turn reasoning conversation, complete assistant messages retain the information needed for follow-up work. Predicted outputs help when revising code or a document with known passages, and a supplied prefix can fix the opening of the answer. Batch workflows can process a collection of independent requests asynchronously.\n\nThe edition is designed for work that continues over many steps. Coding examples include module refactoring, bug fixing, generating checks, updating dependencies and investigating failed development workflows. A useful assignment supplies the affected files, the required behavior and a clear completion criterion. The model can use the results of repository exploration and application-run checks to revise its proposed solution; tools and file access are controlled by the surrounding agent environment.\n\nFor knowledge work, the manufacturer describes research, synthesis and workflows across connected tools. An Ailoot API task can ask for a structured brief assembled from supplied documents, a comparison of technical options or a draft based on retrieved information. Specify which observations support the conclusion and what artifact should complete the task. The model prepares the reasoning and result; the application supplies information access, performs actions and presents the deliverable. The Medium family name remains an updated selection, while the numerical architecture, visual processing and reasoning modes described here belong to the explicitly named Medium 3.5 edition.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Extract obligations from a supplier note\n\nPrepare a document brief from this note: 'The supplier will send samples by 6 June. The buyer must approve the color before production starts. The production lead time is 20 business days after approval. No shipping date has been agreed.' Return obligations, dependency and unresolved information, preserving the distinction between sample delivery and production timing.A concise obligation table and a separate list of unresolved scheduling information.The brief does not calculate an agreed shipping date from a production lead time alone.mistral-medium-latestMistral AI’s Medium family supports document understanding, coding and agents that work across several tool-assisted steps. Its Medium 3.5 edition combines instruction-following and adjustable reasoning in a multimodal model with a 256K context window.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Mistral Medium is Mistral AI’s family for application assistants, document work and software agents. The Medium 3.5 edition combines instruction-following, reasoning and coding in a dense model with 128 billion parameters and a 256K context window. It understands text and visual material and produces text, code or structured results. Its vision encoder was trained to handle variable image sizes and aspect ratios, allowing a task to combine written requirements with screenshots, document pages or other visual evidence.
The model’s features begin with structured output and function calling. JSON output is useful when the response must preserve a machine-readable structure; a custom schema defines required fields and their types. A document task can return a table of extracted facts, a comparison of sections or a summary with open questions. Document question answering combines extraction of the document’s text and structure with language-model understanding, so the question can be related to the relevant content rather than a free-form guess.
In a tool-assisted task, the application describes its functions and the arguments they accept. The model chooses a function, prepares arguments and receives the result after the application executes the action. The conversation can continue with another tool call or a final answer. Parallel tool use lets independent calls be requested together where the application permits it. Tool-choice controls can allow the model to decide, require a tool or keep the task within a text response.
Medium 3.5 adjusts reasoning effort per request. A minimal-thinking setting returns the answer directly; high effort includes a reasoning segment before the final answer and is recommended for agentic and coding tasks. In a multi-turn reasoning conversation, complete assistant messages retain the information needed for follow-up work. Predicted outputs help when revising code or a document with known passages, and a supplied prefix can fix the opening of the answer. Batch workflows can process a collection of independent requests asynchronously.
The edition is designed for work that continues over many steps. Coding examples include module refactoring, bug fixing, generating checks, updating dependencies and investigating failed development workflows. A useful assignment supplies the affected files, the required behavior and a clear completion criterion. The model can use the results of repository exploration and application-run checks to revise its proposed solution; tools and file access are controlled by the surrounding agent environment.
For knowledge work, the manufacturer describes research, synthesis and workflows across connected tools. An Ailoot API task can ask for a structured brief assembled from supplied documents, a comparison of technical options or a draft based on retrieved information. Specify which observations support the conclusion and what artifact should complete the task. The model prepares the reasoning and result; the application supplies information access, performs actions and presents the deliverable. The Medium family name remains an updated selection, while the numerical architecture, visual processing and reasoning modes described here belong to the explicitly named Medium 3.5 edition.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistral-medium-latest | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
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, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
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 $0.00088 / 1K tokens Output · Output tokens $0.0132 / 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": "mistral-medium-latest", "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\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='mistral-medium-latest',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"))\nfrom 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='mistral-medium-latest',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")\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 mistral-medium-latest
Use the method paths shown above.
mistral-medium-latestMistral AI’s Medium family supports document understanding, coding and agents that work across several tool-assisted steps. Its Medium 3.5 edition combines instruction-following and adjustable reasoning in a multimodal model with a 256K context window.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Mistral Medium is Mistral AI’s family for application assistants, document work and software agents. The Medium 3.5 edition combines instruction-following, reasoning and coding in a dense model with 128 billion parameters and a 256K context window. It understands text and visual material and produces text, code or structured results. Its vision encoder was trained to handle variable image sizes and aspect ratios, allowing a task to combine written requirements with screenshots, document pages or other visual evidence.
The model’s features begin with structured output and function calling. JSON output is useful when the response must preserve a machine-readable structure; a custom schema defines required fields and their types. A document task can return a table of extracted facts, a comparison of sections or a summary with open questions. Document question answering combines extraction of the document’s text and structure with language-model understanding, so the question can be related to the relevant content rather than a free-form guess.
In a tool-assisted task, the application describes its functions and the arguments they accept. The model chooses a function, prepares arguments and receives the result after the application executes the action. The conversation can continue with another tool call or a final answer. Parallel tool use lets independent calls be requested together where the application permits it. Tool-choice controls can allow the model to decide, require a tool or keep the task within a text response.
Medium 3.5 adjusts reasoning effort per request. A minimal-thinking setting returns the answer directly; high effort includes a reasoning segment before the final answer and is recommended for agentic and coding tasks. In a multi-turn reasoning conversation, complete assistant messages retain the information needed for follow-up work. Predicted outputs help when revising code or a document with known passages, and a supplied prefix can fix the opening of the answer. Batch workflows can process a collection of independent requests asynchronously.
The edition is designed for work that continues over many steps. Coding examples include module refactoring, bug fixing, generating checks, updating dependencies and investigating failed development workflows. A useful assignment supplies the affected files, the required behavior and a clear completion criterion. The model can use the results of repository exploration and application-run checks to revise its proposed solution; tools and file access are controlled by the surrounding agent environment.
For knowledge work, the manufacturer describes research, synthesis and workflows across connected tools. An Ailoot API task can ask for a structured brief assembled from supplied documents, a comparison of technical options or a draft based on retrieved information. Specify which observations support the conclusion and what artifact should complete the task. The model prepares the reasoning and result; the application supplies information access, performs actions and presents the deliverable. The Medium family name remains an updated selection, while the numerical architecture, visual processing and reasoning modes described here belong to the explicitly named Medium 3.5 edition.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistral-medium-latest | 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 $0.00088 / 1K tokens Output · Output tokens $0.0132 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "mistral-medium-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'mistral-medium-latest', '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': 'mistral-medium-latest', '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 mistral-medium-latest
Use the method paths shown above.
Technical example in English
Extract obligations from a supplier note\n\nPrepare a document brief from this note: 'The supplier will send samples by 6 June. The buyer must approve the color before production starts. The production lead time is 20 business days after approval. No shipping date has been agreed.' Return obligations, dependency and unresolved information, preserving the distinction between sample delivery and production timing.A concise obligation table and a separate list of unresolved scheduling information.The brief does not calculate an agreed shipping date from a production lead time alone.