Codestral 2508 is Mistral AI's coding model for frequent, latency-sensitive completion and generation tasks. It receives source code and textual requirements and returns text that can contain an implementation, completion, correction or explanation. The named 2508 edition focuses on the developer's current editing context rather than being an embedding model or a complete repository agent by itself.
Code completion and the 2508 update
The maker positions Codestral for fill-in-the-middle completion and multi-line editing. The release describes improvements in completion behavior, including reducing runaway generation, together with improvements to instruction following and code tasks in chat mode. These are changes to this Codestral edition; the surrounding coding stack's retrieval and autonomous agent features belong to separately named components.
For a completion task, keep the relevant function signature, declarations and call context with the cursor position. Identify whether the desired result is only the missing code or a complete revised function. An editor-style request might ask for a loop body that preserves the existing return type, retains duplicate values and does not mutate the caller's input. The result then has a concrete insertion point and defined behavior.
Fill-in-the-middle preparation
In the maker's FIM interface, the prompt supplies text or code to complete and an optional suffix supplies the material after the gap. With both supplied, the model fills the space between them. Without a suffix, it continues from the prompt. This differs from an ordinary conversational request that asks for a code change and an explanation; each preparation style defines a different output contract.
A useful gap-completion input includes the function's beginning, the code that must follow the insertion and any relevant type or helper definitions. Preserve indentation and delimiters so that the returned text can fit the existing fragment. If the task needs only the missing body, ask for that body instead of a whole-file rewrite. A separate chat question can explain why the completed fragment meets the requirement.
The official FIM documentation allows an output-token budget and requires the prompt plus that budget to remain within the model's context. It also documents stop sequences and streaming of generated content. Choose a completion workflow when the result must fit the code gap, and a conversational workflow when the task asks for code together with an explanation.
Context and supporting features
The model page gives a 128k context window. Use the context to retain the source and declarations that determine the intended change, while keeping the actual editing task explicit. For a bug-fix request, provide the affected function, a concrete failing input, the expected result and the error or observed behavior. Request the proposed correction separately from its explanation and examples.
The page also lists structured outputs, function calling, predicted outputs, document question answering, response prefixes and batching alongside chat and FIM capabilities. For a structured code-review result, define fields such as the affected symbol, the observed mismatch, the proposed change and the assumption still needing clarification. Mistral's structured-output guidance distinguishes JSON mode from a custom schema, with the desired JSON format stated in the prompt.
Predicted outputs are intended for content where substantial parts of the answer are already known. They can be used when regenerating a document or code file with a limited edit while keeping the remaining content. Specify the exact change and the material that must remain. A response prefix instead supplies a known beginning that generation continues from; it is distinct from both a known full body and a FIM suffix.
A documentation question can use the maker's document-processing workflow to prepare text and structure with OCR before language-model analysis. For programming material already available as text, provide the relevant documentation passages and ask which condition governs the implementation. Keep each extracted requirement associated with its source section so that the generated code or explanation remains connected to the supplied specification.
Tools and practical results
Function calling lets the application provide described operations and their parameters. The model prepares a call, the application executes it and the returned result becomes context for the next answer. A code assistant might receive a lookup result or a calculation result through that cycle, but compiling, testing or applying a proposed change still requires the surrounding development environment.
For a practical correction, define the public interface that must remain compatible and ask for the revised fragment, a concise explanation and example inputs with expected outputs. For a generated function, state the accepted types and edge conditions. For an explanation, supply the input to trace so that the answer relates a concrete execution path to the returned value.
Codestral produces text. It does not inherit code-search vectors from Codestral Embed, repository automation from Devstral or image generation from a visual model. The exact 2508 model page does not publish a parameter count or independent maximum answer length to borrow from another release. Preserve the named edition, coding context and required artifact when preparing a repeatable editor or chat workflow.
", "aboutText": "Codestral 2508 is Mistral AI's coding model for frequent, latency-sensitive completion and generation tasks. It receives source code and textual requirements and returns text that can contain an implementation, completion, correction or explanation. The named 2508 edition focuses on the developer's current editing context rather than being an embedding model or a complete repository agent by itself.\n\nCode completion and the 2508 update\n\nThe maker positions Codestral for fill-in-the-middle completion and multi-line editing. The release describes improvements in completion behavior, including reducing runaway generation, together with improvements to instruction following and code tasks in chat mode. These are changes to this Codestral edition; the surrounding coding stack's retrieval and autonomous agent features belong to separately named components.\n\nFor a completion task, keep the relevant function signature, declarations and call context with the cursor position. Identify whether the desired result is only the missing code or a complete revised function. An editor-style request might ask for a loop body that preserves the existing return type, retains duplicate values and does not mutate the caller's input. The result then has a concrete insertion point and defined behavior.\n\nFill-in-the-middle preparation\n\nIn the maker's FIM interface, the prompt supplies text or code to complete and an optional suffix supplies the material after the gap. With both supplied, the model fills the space between them. Without a suffix, it continues from the prompt. This differs from an ordinary conversational request that asks for a code change and an explanation; each preparation style defines a different output contract.\n\nA useful gap-completion input includes the function's beginning, the code that must follow the insertion and any relevant type or helper definitions. Preserve indentation and delimiters so that the returned text can fit the existing fragment. If the task needs only the missing body, ask for that body instead of a whole-file rewrite. A separate chat question can explain why the completed fragment meets the requirement.\n\nThe official FIM documentation allows an output-token budget and requires the prompt plus that budget to remain within the model's context. It also documents stop sequences and streaming of generated content. Choose a completion workflow when the result must fit the code gap, and a conversational workflow when the task asks for code together with an explanation.\n\nContext and supporting features\n\nThe model page gives a 128k context window. Use the context to retain the source and declarations that determine the intended change, while keeping the actual editing task explicit. For a bug-fix request, provide the affected function, a concrete failing input, the expected result and the error or observed behavior. Request the proposed correction separately from its explanation and examples.\n\nThe page also lists structured outputs, function calling, predicted outputs, document question answering, response prefixes and batching alongside chat and FIM capabilities. For a structured code-review result, define fields such as the affected symbol, the observed mismatch, the proposed change and the assumption still needing clarification. Mistral's structured-output guidance distinguishes JSON mode from a custom schema, with the desired JSON format stated in the prompt.\n\nPredicted outputs are intended for content where substantial parts of the answer are already known. They can be used when regenerating a document or code file with a limited edit while keeping the remaining content. Specify the exact change and the material that must remain. A response prefix instead supplies a known beginning that generation continues from; it is distinct from both a known full body and a FIM suffix.\n\nA documentation question can use the maker's document-processing workflow to prepare text and structure with OCR before language-model analysis. For programming material already available as text, provide the relevant documentation passages and ask which condition governs the implementation. Keep each extracted requirement associated with its source section so that the generated code or explanation remains connected to the supplied specification.\n\nTools and practical results\n\nFunction calling lets the application provide described operations and their parameters. The model prepares a call, the application executes it and the returned result becomes context for the next answer. A code assistant might receive a lookup result or a calculation result through that cycle, but compiling, testing or applying a proposed change still requires the surrounding development environment.\n\nFor a practical correction, define the public interface that must remain compatible and ask for the revised fragment, a concise explanation and example inputs with expected outputs. For a generated function, state the accepted types and edge conditions. For an explanation, supply the input to trace so that the answer relates a concrete execution path to the returned value.\n\nCodestral produces text. It does not inherit code-search vectors from Codestral Embed, repository automation from Devstral or image generation from a visual model. The exact 2508 model page does not publish a parameter count or independent maximum answer length to borrow from another release. Preserve the named edition, coding context and required artifact when preparing a repeatable editor or chat workflow.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Fill a nonmutating loop body\n\nComplete only the missing body in this Python function: 'def doubled(values):\n result = []\n # missing body\n return result'. It must append twice every input value, preserve order and duplicate values, and leave values unchanged. Return the indented insertion followed by assert tests for [2, 2, -1] and []. This is a chat completion request, not a FIM transport request.Only the missing indented loop body, then two tests verifying [4, 4, -2] and an empty result.The insertion fits before return result and never deduplicates or mutates the input.mistralai/codestral-2508Codestral 2508 is Mistral AI's text model for code generation and context-aware completion. It is optimized for frequent editor tasks, including fill-in-the-middle and multi-line edits, with a 128k maker context and documented chat, structured-output and tool features.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Codestral 2508 is Mistral AI's coding model for frequent, latency-sensitive completion and generation tasks. It receives source code and textual requirements and returns text that can contain an implementation, completion, correction or explanation. The named 2508 edition focuses on the developer's current editing context rather than being an embedding model or a complete repository agent by itself.
Code completion and the 2508 update
The maker positions Codestral for fill-in-the-middle completion and multi-line editing. The release describes improvements in completion behavior, including reducing runaway generation, together with improvements to instruction following and code tasks in chat mode. These are changes to this Codestral edition; the surrounding coding stack's retrieval and autonomous agent features belong to separately named components.
For a completion task, keep the relevant function signature, declarations and call context with the cursor position. Identify whether the desired result is only the missing code or a complete revised function. An editor-style request might ask for a loop body that preserves the existing return type, retains duplicate values and does not mutate the caller's input. The result then has a concrete insertion point and defined behavior.
Fill-in-the-middle preparation
In the maker's FIM interface, the prompt supplies text or code to complete and an optional suffix supplies the material after the gap. With both supplied, the model fills the space between them. Without a suffix, it continues from the prompt. This differs from an ordinary conversational request that asks for a code change and an explanation; each preparation style defines a different output contract.
A useful gap-completion input includes the function's beginning, the code that must follow the insertion and any relevant type or helper definitions. Preserve indentation and delimiters so that the returned text can fit the existing fragment. If the task needs only the missing body, ask for that body instead of a whole-file rewrite. A separate chat question can explain why the completed fragment meets the requirement.
The official FIM documentation allows an output-token budget and requires the prompt plus that budget to remain within the model's context. It also documents stop sequences and streaming of generated content. Choose a completion workflow when the result must fit the code gap, and a conversational workflow when the task asks for code together with an explanation.
Context and supporting features
The model page gives a 128k context window. Use the context to retain the source and declarations that determine the intended change, while keeping the actual editing task explicit. For a bug-fix request, provide the affected function, a concrete failing input, the expected result and the error or observed behavior. Request the proposed correction separately from its explanation and examples.
The page also lists structured outputs, function calling, predicted outputs, document question answering, response prefixes and batching alongside chat and FIM capabilities. For a structured code-review result, define fields such as the affected symbol, the observed mismatch, the proposed change and the assumption still needing clarification. Mistral's structured-output guidance distinguishes JSON mode from a custom schema, with the desired JSON format stated in the prompt.
Predicted outputs are intended for content where substantial parts of the answer are already known. They can be used when regenerating a document or code file with a limited edit while keeping the remaining content. Specify the exact change and the material that must remain. A response prefix instead supplies a known beginning that generation continues from; it is distinct from both a known full body and a FIM suffix.
A documentation question can use the maker's document-processing workflow to prepare text and structure with OCR before language-model analysis. For programming material already available as text, provide the relevant documentation passages and ask which condition governs the implementation. Keep each extracted requirement associated with its source section so that the generated code or explanation remains connected to the supplied specification.
Tools and practical results
Function calling lets the application provide described operations and their parameters. The model prepares a call, the application executes it and the returned result becomes context for the next answer. A code assistant might receive a lookup result or a calculation result through that cycle, but compiling, testing or applying a proposed change still requires the surrounding development environment.
For a practical correction, define the public interface that must remain compatible and ask for the revised fragment, a concise explanation and example inputs with expected outputs. For a generated function, state the accepted types and edge conditions. For an explanation, supply the input to trace so that the answer relates a concrete execution path to the returned value.
Codestral produces text. It does not inherit code-search vectors from Codestral Embed, repository automation from Devstral or image generation from a visual model. The exact 2508 model page does not publish a parameter count or independent maximum answer length to borrow from another release. Preserve the named edition, coding context and required artifact when preparing a repeatable editor or chat workflow.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/codestral-2508 | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "mistralai/codestral-2508", "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='mistralai/codestral-2508',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='mistralai/codestral-2508',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 mistralai/codestral-2508
Use the method paths shown above.
mistralai/codestral-2508Codestral 2508 is Mistral AI's text model for code generation and context-aware completion. It is optimized for frequent editor tasks, including fill-in-the-middle and multi-line edits, with a 128k maker context and documented chat, structured-output and tool features.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Codestral 2508 is Mistral AI's coding model for frequent, latency-sensitive completion and generation tasks. It receives source code and textual requirements and returns text that can contain an implementation, completion, correction or explanation. The named 2508 edition focuses on the developer's current editing context rather than being an embedding model or a complete repository agent by itself.
Code completion and the 2508 update
The maker positions Codestral for fill-in-the-middle completion and multi-line editing. The release describes improvements in completion behavior, including reducing runaway generation, together with improvements to instruction following and code tasks in chat mode. These are changes to this Codestral edition; the surrounding coding stack's retrieval and autonomous agent features belong to separately named components.
For a completion task, keep the relevant function signature, declarations and call context with the cursor position. Identify whether the desired result is only the missing code or a complete revised function. An editor-style request might ask for a loop body that preserves the existing return type, retains duplicate values and does not mutate the caller's input. The result then has a concrete insertion point and defined behavior.
Fill-in-the-middle preparation
In the maker's FIM interface, the prompt supplies text or code to complete and an optional suffix supplies the material after the gap. With both supplied, the model fills the space between them. Without a suffix, it continues from the prompt. This differs from an ordinary conversational request that asks for a code change and an explanation; each preparation style defines a different output contract.
A useful gap-completion input includes the function's beginning, the code that must follow the insertion and any relevant type or helper definitions. Preserve indentation and delimiters so that the returned text can fit the existing fragment. If the task needs only the missing body, ask for that body instead of a whole-file rewrite. A separate chat question can explain why the completed fragment meets the requirement.
The official FIM documentation allows an output-token budget and requires the prompt plus that budget to remain within the model's context. It also documents stop sequences and streaming of generated content. Choose a completion workflow when the result must fit the code gap, and a conversational workflow when the task asks for code together with an explanation.
Context and supporting features
The model page gives a 128k context window. Use the context to retain the source and declarations that determine the intended change, while keeping the actual editing task explicit. For a bug-fix request, provide the affected function, a concrete failing input, the expected result and the error or observed behavior. Request the proposed correction separately from its explanation and examples.
The page also lists structured outputs, function calling, predicted outputs, document question answering, response prefixes and batching alongside chat and FIM capabilities. For a structured code-review result, define fields such as the affected symbol, the observed mismatch, the proposed change and the assumption still needing clarification. Mistral's structured-output guidance distinguishes JSON mode from a custom schema, with the desired JSON format stated in the prompt.
Predicted outputs are intended for content where substantial parts of the answer are already known. They can be used when regenerating a document or code file with a limited edit while keeping the remaining content. Specify the exact change and the material that must remain. A response prefix instead supplies a known beginning that generation continues from; it is distinct from both a known full body and a FIM suffix.
A documentation question can use the maker's document-processing workflow to prepare text and structure with OCR before language-model analysis. For programming material already available as text, provide the relevant documentation passages and ask which condition governs the implementation. Keep each extracted requirement associated with its source section so that the generated code or explanation remains connected to the supplied specification.
Tools and practical results
Function calling lets the application provide described operations and their parameters. The model prepares a call, the application executes it and the returned result becomes context for the next answer. A code assistant might receive a lookup result or a calculation result through that cycle, but compiling, testing or applying a proposed change still requires the surrounding development environment.
For a practical correction, define the public interface that must remain compatible and ask for the revised fragment, a concise explanation and example inputs with expected outputs. For a generated function, state the accepted types and edge conditions. For an explanation, supply the input to trace so that the answer relates a concrete execution path to the returned value.
Codestral produces text. It does not inherit code-search vectors from Codestral Embed, repository automation from Devstral or image generation from a visual model. The exact 2508 model page does not publish a parameter count or independent maximum answer length to borrow from another release. Preserve the named edition, coding context and required artifact when preparing a repeatable editor or chat workflow.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/codestral-2508 | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0006 / 1K tokens Output · Output tokens $0.0018 / 1K tokens Input · Cache read $0.00006 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "mistralai/codestral-2508",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'mistralai/codestral-2508', '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': 'mistralai/codestral-2508', '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 mistralai/codestral-2508
Use the method paths shown above.
Technical example in English
Fill a nonmutating loop body\n\nComplete only the missing body in this Python function: 'def doubled(values):\n result = []\n # missing body\n return result'. It must append twice every input value, preserve order and duplicate values, and leave values unchanged. Return the indented insertion followed by assert tests for [2, 2, -1] and []. This is a chat completion request, not a FIM transport request.Only the missing indented loop body, then two tests verifying [4, 4, -2] and an empty result.The insertion fits before return result and never deduplicates or mutates the input.