Ministral 3 8B Instruct 2512 is the middle-sized instruction-tuned model in Mistral AI's Ministral 3 family. The released checkpoint is post-trained for chat and instruction tasks in FP8 precision. It combines a language component with a vision encoder, accepting text and images and returning text rather than an image or audio artifact.
Architecture and key features
The maker lists an 8.4-billion-parameter language model and a 0.4-billion-parameter vision encoder. Its context window is described as 256k, and the published usage notes use a default configured length of 262,144. These describe this 8B-size edition; the smaller 3B and larger 14B family members retain their own component sizes and configurations.
Vision extends the model's input beyond a written conversation. A document image can be paired with a question about a selected field, paragraph or visible relationship. A screenshot can be paired with the user's goal and a request to identify the relevant control. Ask for the observation supporting each conclusion when the answer depends on visual detail, and preserve headings, labels and units needed to interpret a crop.
The card describes multilingual work across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. It also identifies adherence to system instructions, native function calling and JSON output. Define the final language, role and output requirements explicitly rather than leaving the model to infer the intended artifact from a large input.
Document, conversation and agent tasks
The maker's use cases include chat interfaces, everyday assistants, image and document description, translation, content generation and specialized agents. For a document assistant, provide the page and specify the information to return. An extraction request can name the field labels, expected types and treatment of missing values. A useful answer might contain the document identifier, the extracted value and a short explanation tied to the visible source.
For an ongoing conversation, keep the relevant prior answer and ask a focused follow-up. A user can first request a document summary and then ask which statement supports one conclusion. This changes the required output from a general overview to a source-linked explanation while retaining the original material and the purpose of the dialogue.
For multilingual editing, supply the original passage, the intended reader and a terminology list. Request a revised version that retains names, conditions and units, followed by a brief explanation of the changes if useful. For content generation, specify the audience and the facts that should appear before asking for a short draft or alternative wording.
In an agent task, the application provides the tools and their parameters. The model can choose a function and prepare arguments, while the application executes it and returns the result. The card's function example combines an image question with a calculation tool. Keep the tool result separate from the proposed call so that the subsequent written answer uses the information actually returned by the operation.
Recommended preparation and settings
The maker recommends a system instruction that describes the environment, use case and how tools should be used. Keep the set of tools well-defined and limited to the minimum needed for the task. A small lookup workflow can offer the functions relevant to the current question, with clearly named required arguments, instead of presenting a large collection of unrelated operations.
For everyday and production-style use, the card recommends a temperature below 0.1, with higher temperatures available for creative exploration. Treat this as a recommendation for the instruction workflow, not a separate reasoning-mode control. The family has distinct Base, Instruct and Reasoning checkpoints; this entry is the Instruct 2512 edition, not the separate Reasoning release of the same size.
For images, the maker recommends aspect ratios near 1:1 and avoiding very thin or very wide inputs. Crop when needed while retaining the labels and context required by the question. If a page contains several unrelated areas, identify the part to inspect and the result to return. A legible, focused image is more useful than asking the model to guess which small detail in a broad page matters.
Context and edition boundaries
Plan the text, images, conversation and desired answer around the configured context. The usage notes explicitly allow reducing the configured model length when less context is needed, so the full window is not an instruction to fill every request with all available material. The card's 512-token generation example is only an example setting, and the sources read do not establish a separate hard maximum answer length.
The FP8 Instruct checkpoint, the pretrained Base release and the Reasoning release are separate editions. The maker also describes specialization and fine-tuning as use cases. Preserve the named checkpoint and its tokenizer when preparing the conversation, and keep text generation distinct from repository access, function execution or other actions supplied by the surrounding application.
", "aboutText": "Ministral 3 8B Instruct 2512 is the middle-sized instruction-tuned model in Mistral AI's Ministral 3 family. The released checkpoint is post-trained for chat and instruction tasks in FP8 precision. It combines a language component with a vision encoder, accepting text and images and returning text rather than an image or audio artifact.\n\nArchitecture and key features\n\nThe maker lists an 8.4-billion-parameter language model and a 0.4-billion-parameter vision encoder. Its context window is described as 256k, and the published usage notes use a default configured length of 262,144. These describe this 8B-size edition; the smaller 3B and larger 14B family members retain their own component sizes and configurations.\n\nVision extends the model's input beyond a written conversation. A document image can be paired with a question about a selected field, paragraph or visible relationship. A screenshot can be paired with the user's goal and a request to identify the relevant control. Ask for the observation supporting each conclusion when the answer depends on visual detail, and preserve headings, labels and units needed to interpret a crop.\n\nThe card describes multilingual work across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. It also identifies adherence to system instructions, native function calling and JSON output. Define the final language, role and output requirements explicitly rather than leaving the model to infer the intended artifact from a large input.\n\nDocument, conversation and agent tasks\n\nThe maker's use cases include chat interfaces, everyday assistants, image and document description, translation, content generation and specialized agents. For a document assistant, provide the page and specify the information to return. An extraction request can name the field labels, expected types and treatment of missing values. A useful answer might contain the document identifier, the extracted value and a short explanation tied to the visible source.\n\nFor an ongoing conversation, keep the relevant prior answer and ask a focused follow-up. A user can first request a document summary and then ask which statement supports one conclusion. This changes the required output from a general overview to a source-linked explanation while retaining the original material and the purpose of the dialogue.\n\nFor multilingual editing, supply the original passage, the intended reader and a terminology list. Request a revised version that retains names, conditions and units, followed by a brief explanation of the changes if useful. For content generation, specify the audience and the facts that should appear before asking for a short draft or alternative wording.\n\nIn an agent task, the application provides the tools and their parameters. The model can choose a function and prepare arguments, while the application executes it and returns the result. The card's function example combines an image question with a calculation tool. Keep the tool result separate from the proposed call so that the subsequent written answer uses the information actually returned by the operation.\n\nRecommended preparation and settings\n\nThe maker recommends a system instruction that describes the environment, use case and how tools should be used. Keep the set of tools well-defined and limited to the minimum needed for the task. A small lookup workflow can offer the functions relevant to the current question, with clearly named required arguments, instead of presenting a large collection of unrelated operations.\n\nFor everyday and production-style use, the card recommends a temperature below 0.1, with higher temperatures available for creative exploration. Treat this as a recommendation for the instruction workflow, not a separate reasoning-mode control. The family has distinct Base, Instruct and Reasoning checkpoints; this entry is the Instruct 2512 edition, not the separate Reasoning release of the same size.\n\nFor images, the maker recommends aspect ratios near 1:1 and avoiding very thin or very wide inputs. Crop when needed while retaining the labels and context required by the question. If a page contains several unrelated areas, identify the part to inspect and the result to return. A legible, focused image is more useful than asking the model to guess which small detail in a broad page matters.\n\nContext and edition boundaries\n\nPlan the text, images, conversation and desired answer around the configured context. The usage notes explicitly allow reducing the configured model length when less context is needed, so the full window is not an instruction to fill every request with all available material. The card's 512-token generation example is only an example setting, and the sources read do not establish a separate hard maximum answer length.\n\nThe FP8 Instruct checkpoint, the pretrained Base release and the Reasoning release are separate editions. The maker also describes specialization and fine-tuning as use cases. Preserve the named checkpoint and its tokenizer when preparing the conversation, and keep text generation distinct from repository access, function execution or other actions supplied by the surrounding application.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "19fe5bdf85f90c5ff238fb25626f09615b81c4758d1f77b1745e8d3a9cdb8cee", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "mistralai/ministral-8b-2512:batchMinistral 3 8B Instruct 2512 is a multilingual text-and-image instruction model with an 8.4B language component and a 0.4B vision encoder. It supports a 256k maker context, system instructions, JSON and native function calling for assistants and document workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Ministral 3 8B Instruct 2512 is the middle-sized instruction-tuned model in Mistral AI's Ministral 3 family. The released checkpoint is post-trained for chat and instruction tasks in FP8 precision. It combines a language component with a vision encoder, accepting text and images and returning text rather than an image or audio artifact.
Architecture and key features
The maker lists an 8.4-billion-parameter language model and a 0.4-billion-parameter vision encoder. Its context window is described as 256k, and the published usage notes use a default configured length of 262,144. These describe this 8B-size edition; the smaller 3B and larger 14B family members retain their own component sizes and configurations.
Vision extends the model's input beyond a written conversation. A document image can be paired with a question about a selected field, paragraph or visible relationship. A screenshot can be paired with the user's goal and a request to identify the relevant control. Ask for the observation supporting each conclusion when the answer depends on visual detail, and preserve headings, labels and units needed to interpret a crop.
The card describes multilingual work across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. It also identifies adherence to system instructions, native function calling and JSON output. Define the final language, role and output requirements explicitly rather than leaving the model to infer the intended artifact from a large input.
Document, conversation and agent tasks
The maker's use cases include chat interfaces, everyday assistants, image and document description, translation, content generation and specialized agents. For a document assistant, provide the page and specify the information to return. An extraction request can name the field labels, expected types and treatment of missing values. A useful answer might contain the document identifier, the extracted value and a short explanation tied to the visible source.
For an ongoing conversation, keep the relevant prior answer and ask a focused follow-up. A user can first request a document summary and then ask which statement supports one conclusion. This changes the required output from a general overview to a source-linked explanation while retaining the original material and the purpose of the dialogue.
For multilingual editing, supply the original passage, the intended reader and a terminology list. Request a revised version that retains names, conditions and units, followed by a brief explanation of the changes if useful. For content generation, specify the audience and the facts that should appear before asking for a short draft or alternative wording.
In an agent task, the application provides the tools and their parameters. The model can choose a function and prepare arguments, while the application executes it and returns the result. The card's function example combines an image question with a calculation tool. Keep the tool result separate from the proposed call so that the subsequent written answer uses the information actually returned by the operation.
Recommended preparation and settings
The maker recommends a system instruction that describes the environment, use case and how tools should be used. Keep the set of tools well-defined and limited to the minimum needed for the task. A small lookup workflow can offer the functions relevant to the current question, with clearly named required arguments, instead of presenting a large collection of unrelated operations.
For everyday and production-style use, the card recommends a temperature below 0.1, with higher temperatures available for creative exploration. Treat this as a recommendation for the instruction workflow, not a separate reasoning-mode control. The family has distinct Base, Instruct and Reasoning checkpoints; this entry is the Instruct 2512 edition, not the separate Reasoning release of the same size.
For images, the maker recommends aspect ratios near 1:1 and avoiding very thin or very wide inputs. Crop when needed while retaining the labels and context required by the question. If a page contains several unrelated areas, identify the part to inspect and the result to return. A legible, focused image is more useful than asking the model to guess which small detail in a broad page matters.
Context and edition boundaries
Plan the text, images, conversation and desired answer around the configured context. The usage notes explicitly allow reducing the configured model length when less context is needed, so the full window is not an instruction to fill every request with all available material. The card's 512-token generation example is only an example setting, and the sources read do not establish a separate hard maximum answer length.
The FP8 Instruct checkpoint, the pretrained Base release and the Reasoning release are separate editions. The maker also describes specialization and fine-tuning as use cases. Preserve the named checkpoint and its tokenizer when preparing the conversation, and keep text generation distinct from repository access, function execution or other actions supplied by the surrounding application.
POST /v1/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/ministral-8b-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/ministral-8b-2512:batch, mistralai/ministral-8b-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 $0.00015 / 1K tokens Output · Output tokens $0.00015 / 1K tokens Input · Cache read $0.000015 / 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 $0.00015 / 1K tokens Output · Output tokens $0.00015 / 1K tokens Input · Cache read $0.000015 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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/ministral-8b-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/ministral-8b-2512:batch', 'mistralai/ministral-8b-2512']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"mistralai/ministral-8b-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/ministral-8b-2512:batch
Use the method paths shown above.