Mistral Large 3 2512 is the instruction-tuned Mistral AI edition for general conversation, visual understanding and enterprise work. Its sparse mixture-of-experts architecture has 675 billion total parameters, with 41 billion active, and a 256k context window. Text and images can contribute to the same task, while the answer is text, code or a structured result.
Capabilities and input preparation
The model combines a language component with a vision encoder. Give an image a clear role: a page to read, a diagram to interpret or a screen to explain. For a crowded screen, provide a crop containing the relevant controls and retain the surrounding labels needed to understand them. Mistral recommends image shapes close to square; break an exceptionally narrow or wide source into useful regions instead of shrinking important details into an unreadable strip.
The maker lists multilingual conversation across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. State the output language, intended reader and terminology to preserve. For a support article, ask for a localized explanation with product names unchanged and a short list of uncertain translations to resolve before publication.
Long documents and enterprise knowledge
Use the context to bring related material together: policy text, a specification, existing decisions and the question that needs an answer. Label each source and its revision. A useful request is: “Compare these three onboarding policies. Return a table of shared requirements, conflicts and unanswered questions, with the source section for each finding.” The resulting artifact is a comparison grounded in the supplied passages rather than a generic summary of the topic.
For a document question, identify the desired level of detail and request the supporting passage next to the conclusion. Mistral's Document QnA workflow first extracts text and document structure with OCR, then uses a language model to interpret that material. Preserve page numbers and headings when preparing extracted text so the answer can point back to the original. A document URL, extracted text and image evidence have different preparation requirements; choose the input form supported by the application you are using.
Coding and everyday assistance
Mistral positions Large 3 for coding assistance as well as daily conversation. Supply the relevant functions, expected behavior and observed failure. For example: “This parser drops the last field when the input ends without a newline. Explain the failing branch, propose the smallest change and return the corrected function with three representative input/output examples.” The useful result is a proposed change tied to the actual code and a set of cases the developer can run.
For a business draft, provide the facts and desired artifact together. A request to turn meeting notes into a decision memo should name the decision, alternatives, unresolved assumptions and next action. Ask the model to keep source facts separate from recommendations and to mark missing information within the appropriate section. This makes the output easier to revise than an undifferentiated narrative.
Tools and structured results
Native function calling lets the model choose a supplied function and prepare its arguments. The application runs that function and returns its result; the model then uses the returned information in the answer. Keep tools specific and expose only those needed for the task. A stock-check assistant can receive a product reference, request a stock lookup and explain the returned quantity and warehouse. Validate the selected function and arguments before executing the request, and retain the tool response alongside the final answer.
For extraction, specify the fields, types and treatment of missing values. Mistral documents JSON mode and custom structured output; its JSON mode requires an explicit instruction to return JSON, while a custom schema supplies stronger format control. An invoice summary can ask for supplier, invoice number, due date, currency and line items, with missing values represented consistently. Inspect the extracted values against the document, even when the response matches the requested structure.
Settings and edition differences
Define the role, task and tool rules in the system instructions. Mistral recommends a temperature below 0.1 for production and everyday use of this checkpoint, with higher values explored for creative tasks. Separate a request for varied ideas from a request for a reproducible extraction: give the former room for alternatives and the latter explicit fields and source constraints.
This instruction edition is separate from the Large 3 base checkpoint and from dedicated reasoning models. The maker identifies strict reasoning and specialized visual work as areas where a model designed specifically for those tasks can be preferable. For a difficult calculation, supply the assumptions and units, ask for intermediate checks and use an available calculation tool to verify the result. For dense visual evidence, prepare readable regions and compare the answer with the source before using it in a report.
", "aboutText": "Mistral Large 3 2512 is the instruction-tuned Mistral AI edition for general conversation, visual understanding and enterprise work. Its sparse mixture-of-experts architecture has 675 billion total parameters, with 41 billion active, and a 256k context window. Text and images can contribute to the same task, while the answer is text, code or a structured result.\n\nCapabilities and input preparation\n\nThe model combines a language component with a vision encoder. Give an image a clear role: a page to read, a diagram to interpret or a screen to explain. For a crowded screen, provide a crop containing the relevant controls and retain the surrounding labels needed to understand them. Mistral recommends image shapes close to square; break an exceptionally narrow or wide source into useful regions instead of shrinking important details into an unreadable strip.\n\nThe maker lists multilingual conversation across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. State the output language, intended reader and terminology to preserve. For a support article, ask for a localized explanation with product names unchanged and a short list of uncertain translations to resolve before publication.\n\nLong documents and enterprise knowledge\n\nUse the context to bring related material together: policy text, a specification, existing decisions and the question that needs an answer. Label each source and its revision. A useful request is: “Compare these three onboarding policies. Return a table of shared requirements, conflicts and unanswered questions, with the source section for each finding.” The resulting artifact is a comparison grounded in the supplied passages rather than a generic summary of the topic.\n\nFor a document question, identify the desired level of detail and request the supporting passage next to the conclusion. Mistral's Document QnA workflow first extracts text and document structure with OCR, then uses a language model to interpret that material. Preserve page numbers and headings when preparing extracted text so the answer can point back to the original. A document URL, extracted text and image evidence have different preparation requirements; choose the input form supported by the application you are using.\n\nCoding and everyday assistance\n\nMistral positions Large 3 for coding assistance as well as daily conversation. Supply the relevant functions, expected behavior and observed failure. For example: “This parser drops the last field when the input ends without a newline. Explain the failing branch, propose the smallest change and return the corrected function with three representative input/output examples.” The useful result is a proposed change tied to the actual code and a set of cases the developer can run.\n\nFor a business draft, provide the facts and desired artifact together. A request to turn meeting notes into a decision memo should name the decision, alternatives, unresolved assumptions and next action. Ask the model to keep source facts separate from recommendations and to mark missing information within the appropriate section. This makes the output easier to revise than an undifferentiated narrative.\n\nTools and structured results\n\nNative function calling lets the model choose a supplied function and prepare its arguments. The application runs that function and returns its result; the model then uses the returned information in the answer. Keep tools specific and expose only those needed for the task. A stock-check assistant can receive a product reference, request a stock lookup and explain the returned quantity and warehouse. Validate the selected function and arguments before executing the request, and retain the tool response alongside the final answer.\n\nFor extraction, specify the fields, types and treatment of missing values. Mistral documents JSON mode and custom structured output; its JSON mode requires an explicit instruction to return JSON, while a custom schema supplies stronger format control. An invoice summary can ask for supplier, invoice number, due date, currency and line items, with missing values represented consistently. Inspect the extracted values against the document, even when the response matches the requested structure.\n\nSettings and edition differences\n\nDefine the role, task and tool rules in the system instructions. Mistral recommends a temperature below 0.1 for production and everyday use of this checkpoint, with higher values explored for creative tasks. Separate a request for varied ideas from a request for a reproducible extraction: give the former room for alternatives and the latter explicit fields and source constraints.\n\nThis instruction edition is separate from the Large 3 base checkpoint and from dedicated reasoning models. The maker identifies strict reasoning and specialized visual work as areas where a model designed specifically for those tasks can be preferable. For a difficult calculation, supply the assumptions and units, ask for intermediate checks and use an available calculation tool to verify the result. For dense visual evidence, prepare readable regions and compare the answer with the source before using it in a report.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "f67d46cdd41735b2113f2d1d7dd800f62261e86a45759159888f026a492fb5c4", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "mistralai/mistral-large-2512:batchMistral Large 3 2512 is a multilingual text-and-image model for long documents, enterprise knowledge, coding and tool-assisted work. Its 256k context, native function calling and structured output help turn supplied evidence into answers, records and proposed actions.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Mistral Large 3 2512 is the instruction-tuned Mistral AI edition for general conversation, visual understanding and enterprise work. Its sparse mixture-of-experts architecture has 675 billion total parameters, with 41 billion active, and a 256k context window. Text and images can contribute to the same task, while the answer is text, code or a structured result.
Capabilities and input preparation
The model combines a language component with a vision encoder. Give an image a clear role: a page to read, a diagram to interpret or a screen to explain. For a crowded screen, provide a crop containing the relevant controls and retain the surrounding labels needed to understand them. Mistral recommends image shapes close to square; break an exceptionally narrow or wide source into useful regions instead of shrinking important details into an unreadable strip.
The maker lists multilingual conversation across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. State the output language, intended reader and terminology to preserve. For a support article, ask for a localized explanation with product names unchanged and a short list of uncertain translations to resolve before publication.
Long documents and enterprise knowledge
Use the context to bring related material together: policy text, a specification, existing decisions and the question that needs an answer. Label each source and its revision. A useful request is: “Compare these three onboarding policies. Return a table of shared requirements, conflicts and unanswered questions, with the source section for each finding.” The resulting artifact is a comparison grounded in the supplied passages rather than a generic summary of the topic.
For a document question, identify the desired level of detail and request the supporting passage next to the conclusion. Mistral's Document QnA workflow first extracts text and document structure with OCR, then uses a language model to interpret that material. Preserve page numbers and headings when preparing extracted text so the answer can point back to the original. A document URL, extracted text and image evidence have different preparation requirements; choose the input form supported by the application you are using.
Coding and everyday assistance
Mistral positions Large 3 for coding assistance as well as daily conversation. Supply the relevant functions, expected behavior and observed failure. For example: “This parser drops the last field when the input ends without a newline. Explain the failing branch, propose the smallest change and return the corrected function with three representative input/output examples.” The useful result is a proposed change tied to the actual code and a set of cases the developer can run.
For a business draft, provide the facts and desired artifact together. A request to turn meeting notes into a decision memo should name the decision, alternatives, unresolved assumptions and next action. Ask the model to keep source facts separate from recommendations and to mark missing information within the appropriate section. This makes the output easier to revise than an undifferentiated narrative.
Tools and structured results
Native function calling lets the model choose a supplied function and prepare its arguments. The application runs that function and returns its result; the model then uses the returned information in the answer. Keep tools specific and expose only those needed for the task. A stock-check assistant can receive a product reference, request a stock lookup and explain the returned quantity and warehouse. Validate the selected function and arguments before executing the request, and retain the tool response alongside the final answer.
For extraction, specify the fields, types and treatment of missing values. Mistral documents JSON mode and custom structured output; its JSON mode requires an explicit instruction to return JSON, while a custom schema supplies stronger format control. An invoice summary can ask for supplier, invoice number, due date, currency and line items, with missing values represented consistently. Inspect the extracted values against the document, even when the response matches the requested structure.
Settings and edition differences
Define the role, task and tool rules in the system instructions. Mistral recommends a temperature below 0.1 for production and everyday use of this checkpoint, with higher values explored for creative tasks. Separate a request for varied ideas from a request for a reproducible extraction: give the former room for alternatives and the latter explicit fields and source constraints.
This instruction edition is separate from the Large 3 base checkpoint and from dedicated reasoning models. The maker identifies strict reasoning and specialized visual work as areas where a model designed specifically for those tasks can be preferable. For a difficult calculation, supply the assumptions and units, ask for intermediate checks and use an available calculation tool to verify the result. For dense visual evidence, prepare readable regions and compare the answer with the source before using it in a report.
POST /v1/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/mistral-large-2512: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/mistral-large-2512:batch, mistralai/mistral-large-2512 | 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 $0.0005 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read $0.00005 / 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 $0.0005 / 1K tokens Output · Output tokens $0.0015 / 1K tokens Input · Cache read $0.00005 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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/mistral-large-2512: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/mistral-large-2512:batch', 'mistralai/mistral-large-2512']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"mistralai/mistral-large-2512: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/mistral-large-2512:batch
Use the method paths shown above.