Devstral Small 1.1 is Mistral AI’s fixed text-only model for software-engineering agents. It is fine-tuned from Mistral Small 3.1 with the vision encoder removed, concentrating the model on repository exploration, tools and multi-file code changes. The release has 24 billion parameters and a context window of up to 128K tokens. Its output consists of text, code or instructions for the functions made available by the agent application.
Version 1.1 keeps the preceding Small release’s architecture while extending how it works with prompts and coding environments. The manufacturer describes better generalization across different agent scaffolds and adds support for Mistral’s function-calling format alongside XML-based formats. These are ways to express and interpret agent interactions; the supplied tools and execution environment determine which repository actions are available.
The key model properties include the 24B size, Apache 2.0 weights and the Tekken tokenizer with a vocabulary of approximately 131K entries. The fixed 1.1 edition is identified by its own release name and retains its 128K context. Textual project material can include source files, task requirements, previous actions and the returned results of searches or commands. This allows the next proposed change to be based on what the agent has learned about the project.
A practical workflow begins with a specific engineering goal. Ask the agent to locate the path that handles a request, identify the relevant functions and propose coordinated edits to the files involved. Supply the expected behavior, compatibility requirements and examples that distinguish a correct result. For a defect, include the observed error and the input that reproduces it. For a refactor, name the interfaces and behavior that must remain stable. The useful artifact is the proposed patch together with an explanation of its dependencies and the checks required to validate it.
The model also supports structured responses and document questions. A code-review task can return separate fields for affected components, proposed changes and follow-up checks. A technical-document task can identify the requirements relevant to the implementation. Prefix continuation can establish the beginning of a response, while asynchronous batch workflows can handle independent code-analysis tasks. These features complement the main repository-and-tools specialization.
In an Ailoot API agent, provide the tool descriptions and their argument schemas together with the task. Devstral can select a function and prepare arguments; the application executes the function, returns its output and keeps the conversation going. The model does not independently obtain file access or execute its suggested code. Use the returned tool results to refine the proposed edits and finish with the code artifact, the explanation and the evidence supplied by the environment. The Small 1.1 identity remains separate from later Devstral editions and from general-purpose Mistral Small releases.
", "aboutText": "Devstral Small 1.1 is Mistral AI’s fixed text-only model for software-engineering agents. It is fine-tuned from Mistral Small 3.1 with the vision encoder removed, concentrating the model on repository exploration, tools and multi-file code changes. The release has 24 billion parameters and a context window of up to 128K tokens. Its output consists of text, code or instructions for the functions made available by the agent application.\n\nVersion 1.1 keeps the preceding Small release’s architecture while extending how it works with prompts and coding environments. The manufacturer describes better generalization across different agent scaffolds and adds support for Mistral’s function-calling format alongside XML-based formats. These are ways to express and interpret agent interactions; the supplied tools and execution environment determine which repository actions are available.\n\nThe key model properties include the 24B size, Apache 2.0 weights and the Tekken tokenizer with a vocabulary of approximately 131K entries. The fixed 1.1 edition is identified by its own release name and retains its 128K context. Textual project material can include source files, task requirements, previous actions and the returned results of searches or commands. This allows the next proposed change to be based on what the agent has learned about the project.\n\nA practical workflow begins with a specific engineering goal. Ask the agent to locate the path that handles a request, identify the relevant functions and propose coordinated edits to the files involved. Supply the expected behavior, compatibility requirements and examples that distinguish a correct result. For a defect, include the observed error and the input that reproduces it. For a refactor, name the interfaces and behavior that must remain stable. The useful artifact is the proposed patch together with an explanation of its dependencies and the checks required to validate it.\n\nThe model also supports structured responses and document questions. A code-review task can return separate fields for affected components, proposed changes and follow-up checks. A technical-document task can identify the requirements relevant to the implementation. Prefix continuation can establish the beginning of a response, while asynchronous batch workflows can handle independent code-analysis tasks. These features complement the main repository-and-tools specialization.\n\nIn an Ailoot API agent, provide the tool descriptions and their argument schemas together with the task. Devstral can select a function and prepare arguments; the application executes the function, returns its output and keeps the conversation going. The model does not independently obtain file access or execute its suggested code. Use the returned tool results to refine the proposed edits and finish with the code artifact, the explanation and the evidence supplied by the environment. The Small 1.1 identity remains separate from later Devstral editions and from general-purpose Mistral Small releases.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Coordinate a renamed function\n\nTwo Python files contain: service.py: def load_user(uid): return {'id': uid}; handler.py: from service import load_user; def handle(uid): return load_user(uid). Rename load_user to fetch_user in both files while preserving handle's interface and returned value. Return separate changed file blocks and one regression assertion for handle(7).Two named Python file blocks and an assertion preserving handle behavior.The import, definition and call must all use fetch_user while handle remains callable by its original name.devstral-small-2507Devstral Small 1.1 is Mistral AI’s 24B text-only coding-agent model for exploring repositories and preparing coordinated edits across files. Version 1.1 adds broader prompt compatibility and supports function-calling and XML-based agent formats, with a 128K context window.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Devstral Small 1.1 is Mistral AI’s fixed text-only model for software-engineering agents. It is fine-tuned from Mistral Small 3.1 with the vision encoder removed, concentrating the model on repository exploration, tools and multi-file code changes. The release has 24 billion parameters and a context window of up to 128K tokens. Its output consists of text, code or instructions for the functions made available by the agent application.
Version 1.1 keeps the preceding Small release’s architecture while extending how it works with prompts and coding environments. The manufacturer describes better generalization across different agent scaffolds and adds support for Mistral’s function-calling format alongside XML-based formats. These are ways to express and interpret agent interactions; the supplied tools and execution environment determine which repository actions are available.
The key model properties include the 24B size, Apache 2.0 weights and the Tekken tokenizer with a vocabulary of approximately 131K entries. The fixed 1.1 edition is identified by its own release name and retains its 128K context. Textual project material can include source files, task requirements, previous actions and the returned results of searches or commands. This allows the next proposed change to be based on what the agent has learned about the project.
A practical workflow begins with a specific engineering goal. Ask the agent to locate the path that handles a request, identify the relevant functions and propose coordinated edits to the files involved. Supply the expected behavior, compatibility requirements and examples that distinguish a correct result. For a defect, include the observed error and the input that reproduces it. For a refactor, name the interfaces and behavior that must remain stable. The useful artifact is the proposed patch together with an explanation of its dependencies and the checks required to validate it.
The model also supports structured responses and document questions. A code-review task can return separate fields for affected components, proposed changes and follow-up checks. A technical-document task can identify the requirements relevant to the implementation. Prefix continuation can establish the beginning of a response, while asynchronous batch workflows can handle independent code-analysis tasks. These features complement the main repository-and-tools specialization.
In an Ailoot API agent, provide the tool descriptions and their argument schemas together with the task. Devstral can select a function and prepare arguments; the application executes the function, returns its output and keeps the conversation going. The model does not independently obtain file access or execute its suggested code. Use the returned tool results to refine the proposed edits and finish with the code artifact, the explanation and the evidence supplied by the environment. The Small 1.1 identity remains separate from later Devstral editions and from general-purpose Mistral Small releases.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits devstral-small-2507 | 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 $0.00022 / 1K tokens Output · Output tokens $0.00066 / 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": "devstral-small-2507", "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='devstral-small-2507',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='devstral-small-2507',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 devstral-small-2507
Use the method paths shown above.
devstral-small-2507Devstral Small 1.1 is Mistral AI’s 24B text-only coding-agent model for exploring repositories and preparing coordinated edits across files. Version 1.1 adds broader prompt compatibility and supports function-calling and XML-based agent formats, with a 128K context window.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Devstral Small 1.1 is Mistral AI’s fixed text-only model for software-engineering agents. It is fine-tuned from Mistral Small 3.1 with the vision encoder removed, concentrating the model on repository exploration, tools and multi-file code changes. The release has 24 billion parameters and a context window of up to 128K tokens. Its output consists of text, code or instructions for the functions made available by the agent application.
Version 1.1 keeps the preceding Small release’s architecture while extending how it works with prompts and coding environments. The manufacturer describes better generalization across different agent scaffolds and adds support for Mistral’s function-calling format alongside XML-based formats. These are ways to express and interpret agent interactions; the supplied tools and execution environment determine which repository actions are available.
The key model properties include the 24B size, Apache 2.0 weights and the Tekken tokenizer with a vocabulary of approximately 131K entries. The fixed 1.1 edition is identified by its own release name and retains its 128K context. Textual project material can include source files, task requirements, previous actions and the returned results of searches or commands. This allows the next proposed change to be based on what the agent has learned about the project.
A practical workflow begins with a specific engineering goal. Ask the agent to locate the path that handles a request, identify the relevant functions and propose coordinated edits to the files involved. Supply the expected behavior, compatibility requirements and examples that distinguish a correct result. For a defect, include the observed error and the input that reproduces it. For a refactor, name the interfaces and behavior that must remain stable. The useful artifact is the proposed patch together with an explanation of its dependencies and the checks required to validate it.
The model also supports structured responses and document questions. A code-review task can return separate fields for affected components, proposed changes and follow-up checks. A technical-document task can identify the requirements relevant to the implementation. Prefix continuation can establish the beginning of a response, while asynchronous batch workflows can handle independent code-analysis tasks. These features complement the main repository-and-tools specialization.
In an Ailoot API agent, provide the tool descriptions and their argument schemas together with the task. Devstral can select a function and prepare arguments; the application executes the function, returns its output and keeps the conversation going. The model does not independently obtain file access or execute its suggested code. Use the returned tool results to refine the proposed edits and finish with the code artifact, the explanation and the evidence supplied by the environment. The Small 1.1 identity remains separate from later Devstral editions and from general-purpose Mistral Small releases.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits devstral-small-2507 | 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 $0.00022 / 1K tokens Output · Output tokens $0.00066 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "devstral-small-2507",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'devstral-small-2507', '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': 'devstral-small-2507', '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 devstral-small-2507
Use the method paths shown above.
Technical example in English
Coordinate a renamed function\n\nTwo Python files contain: service.py: def load_user(uid): return {'id': uid}; handler.py: from service import load_user; def handle(uid): return load_user(uid). Rename load_user to fetch_user in both files while preserving handle's interface and returned value. Return separate changed file blocks and one regression assertion for handle(7).Two named Python file blocks and an assertion preserving handle behavior.The import, definition and call must all use fetch_user while handle remains callable by its original name.