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": null, "copy_sha256": null, "copy_source": null, "html_sha256": "0508f0c9c4572c719236919103b171498d2cc4b8c81cbb08e0b82532e61b4f3e", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "mistralai/codestral-2508:batchCodestral 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/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/codestral-2508:batch | Default— |
Fieldrequests | Typebatch_item[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldrequests[].custom_id | Typestring | RequirementRequired | Values & limits | Default— |
Fieldrequests[].body.messages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldcompletion_window | Typestring | RequirementOptional | Values & limits 24h | Default— |
Fieldprovider | Typeobject|null | RequirementOptional | Values & limits | Default— |
Fieldrequests[].body.model | Typestring | RequirementOptional | Values & limits mistralai/codestral-2508:batch, mistralai/codestral-2508 | Default— |
Fieldprovider.only | Typestring[] | RequirementOptional | Values & limits mistral | Default— |
Fieldprovider.allow_fallbacks | Typeboolean|null | RequirementOptional | Values & limits false, null | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractendpoint, model, requests |
Parametermaterial_types | Contracttext |
Parameterrepresentation | Contractinline requests[]; each item has custom_id and body |
Parameterrequest_endpoint_profile | Contract/v1/chat/completions |
Parameterfile_material | ContractCaller may read actual UTF-8 JSONL locally into requests[]. No upload purpose, input_file_id or file reference is used. Missing/empty customer material stops preparation before POST. |
Parameterserialization_order | Contractendpoint, model, provider (if supplied), completion_window (if supplied), requests last |
Parameteroptional_controls | Contractprovider.only, provider.allow_fallbacks, completion_window |
Parameterschema_scope | ContractText Chat Completions bodies in the documented inline Batch envelope. |
Lifecycle: asynchronous
GET /v1/batches/{operation_id} · actual original create response.id; same user/key/group
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 $0.0003 / 1K tokens Output · Output tokens $0.0009 / 1K tokens Input · Cache read $0.00003 / 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 $0.0003 / 1K tokens Output · Output tokens $0.0009 / 1K tokens Input · Cache read $0.00003 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json\nfrom pathlib import Path\nbody=json.loads('{"endpoint":"/v1/chat/completions","model":"mistralai/codestral-2508:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')\nPath("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json\nimport json, os\nfrom pathlib import Path\nfrom urllib.request import Request, urlopen\n\ndef prepare_requests(customer_path):\n path = Path(customer_path)\n if not path.is_file(): raise ValueError("Customer JSONL file is missing")\n items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]\n if not items: raise ValueError("Customer JSONL contains no requests")\n ids = set()\n for item in items:\n if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")\n cid = item["custom_id"]\n if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")\n ids.add(cid)\n if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")\n supplied_model = item["body"].get("model")\n if supplied_model is not None and supplied_model not in ['mistralai/codestral-2508:batch', 'mistralai/codestral-2508']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"mistralai/codestral-2508:batch", "requests":items}\n\nif __name__ == "__main__":\n material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")\n if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")\n body = prepare_requests(material)\n key = os.environ.get("AILOOT_API_KEY", "")\n if not key: raise SystemExit("Set AILOOT_API_KEY")\n request = Request("https://api.ailoot.ai/v1/batches", data=json.dumps(body, ensure_ascii=False).encode("utf-8"), headers={"Authorization":"Bearer " + key, "Content-Type":"application/json"}, method="POST")\n with urlopen(request) as response:\n raw = response.read()\n Path("batch-create-response.json").write_bytes(raw)\n print(raw.decode("utf-8"))\nimport json\nfrom pathlib import Path\nraw = Path("batch-status-response.json").read_bytes()\nresponse = json.loads(raw)\nif response.get("status") != "completed":\n raise SystemExit("Keep the original operation; read its status without resubmitting")\nresults = response.get("results")\nif not isinstance(results, list) or not results:\n raise SystemExit("Preserve the original response; completed results are missing")\nPath("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\\n" for item in results), encoding="utf-8")\n# Preserve batch-status-response.json with aggregate usage and every result/error.\ninline results[].response.body JSON
GET /v1/batches/{operation_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nBATCH_OPERATION_ID="$(python3 - <<'PY_ID'\nimport json\nfrom pathlib import Path\nfrom urllib.parse import quote\nresponse = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))\noperation = response.get("id")\nif not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")\nprint(quote(operation, safe=""))\nPY_ID\n)"\ncurl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json\nKeep every complete returned response, file and content part.
Preserve the full original status JSON, every result/custom_id and complete response or error body. Save completed results[] as JSONL without filtering.
Preserve the original operation ID. 503 result_pending_check has X-Should-Retry:false; never resubmit an unknown original batch.
| Field | Type |
|---|---|
Fieldresults[].custom_id | Typestring |
Fieldresults[].response.body.choices[].message.content | Typestring|null |
Fieldresults[].error | Typeobject|null |
Fieldusage | Typeobject|null |
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldendpoint | Typestring |
Fieldstatus | Typestring |
Fieldrequest_counts | Typeobject |
Fieldresults[].id | Typestring |
Fieldresults[].response.status_code | Typeinteger |
Fieldresults[].response.request_id | Typestring |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid documented inline envelope, duplicate keys, item identity, or configured model capabilities conflict. | Next stepCorrect the indicated fields before any new submission. |
Response401 · authentication_error | ConditionInvalid API key. | Next stepUse the authorized Ailoot key. |
Response403 · permission_error | ConditionBatch group unavailable. | Next stepCheck key/group entitlement. |
Response422 · invalid_request_error | ConditionBatch uses balance billing; subscription creation is rejected. | Next stepUse the authorized balance-billed group. |
Response422 · price_unavailable | ConditionBatch input cannot be quoted. | Next stepPreserve response; use documented family input. |
Response402 · billing_error | ConditionBatch reserve unavailable. | Next stepResolve admission before a new request. |
Response503 · price_unavailable | ConditionBatch method price/native rates unavailable. | Next stepPreserve response; do not substitute prices. |
Response503 · api_error | ConditionExact supplier route unavailable. | Next stepPreserve response for route repair. |
Response503 · result_pending_check | ConditionOriginal result requires checking; X-Should-Retry:false. | Next stepKeep original operation_id/id and read its status; never repeat original POST. |
Response404 · not_found | ConditionBatch status operation absent or owned by another identity. | Next stepUse exact original id and same user/key/group. |
Ailoot Ultra mistralai/codestral-2508:batch
Use the method paths shown above.