Mistral Medium 3.5 is Mistral AI's dense 128B model combining instruction following, reasoning and coding in one set of weights. It receives text and images and returns text. Its maker positions it for agentic and coding tasks, while the same checkpoint can produce a direct conversational answer or use its documented reasoning mode for a more involved task.
Unified model and visual capabilities
The released card specifies 128 billion dense parameters and a 256k context window. Its vision encoder is trained to handle different image sizes and aspect ratios. Images can contribute evidence to a coding or document task: a screenshot of an interface, a diagram accompanying a requirement or a page containing information the model must interpret. The result can be a written explanation, code or structured fields rather than a generated image.
The maker describes multilingual support across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. It also highlights system-prompt adherence and native function calling with JSON output. Set the audience, language and response rules separately from the material being examined so that a source document's wording remains evidence for the task.
Reasoning settings
The card documents two reasoning-effort values per request. The value none gives an instruction-style answer without reasoning mode; high enables reasoning and is recommended for complex prompts and agentic coding. This is a mode choice within the same Medium 3.5 checkpoint, rather than a different model identity.
For high reasoning effort, the maker recommends temperature 0.7 and top-p 0.95. For none, it documents temperatures between 0.0 and 0.7 depending on the task and leaves top-p unset or at 1.0. These are release recommendations. Choose the output purpose first: a concise field extraction, a code change with an explanation or a comparison that requires several justified decisions.
A practical reasoning task can supply a requirement, the implementation and the input that exposes a mismatch. Ask the model to identify the relevant path, explain the discrepancy and propose the smallest change that preserves the public interface. Request the revised code separately from the assumptions and examples. The model generates a proposal; compiling, running and applying it require the development environment connected to the agent.
Tools, documents and predictable edits
The maker's service card also lists structured outputs, predicted outputs, document questions, response prefixes, batch processing and agent conversations. For extraction, define the field names, types and treatment of missing values. JSON mode supplies a structured representation, while a custom schema defines a more specific shape in the connected interface. Ask for only the record when a program will parse it and a separate explanation when a person needs to inspect the interpretation.
Document question answering combines OCR-prepared text and structure with language-model understanding. For a comparison, identify the documents and the condition to evaluate, then request each finding with its source location. A useful output can separate shared requirements, disagreements and open questions. Direct image questions can focus on a chart label or interface element; preserving headings, units and image roles helps keep the answer tied to the supplied material.
Predicted outputs are documented for tasks where much of the desired answer is already known, such as regenerating a code file or document with limited edits. Supply the existing content and identify the specific change, preserving the parts that should remain. This is different from asking for an entirely new artifact or continuing from a known response prefix. The service features describe submission and formatting options; they do not change the model's text-and-image input or text output.
In a tool-assisted workflow, the application supplies function descriptions and parameters, the model chooses a function and prepares arguments, and the application executes it and returns the result. A calculation tool can receive values read from a supplied image, while a lookup tool can provide the missing information for the next answer. Keep the tool result associated with the operation that produced it. Agentic reasoning relies on that environment rather than on text generation alone performing external actions.
Preparing long sessions
Use the 256k context for related source code, documents and conversation history, and keep the actual goal explicit. A coding session can retain the module, its caller and the requirements while asking for one defined change. A document session can keep the relevant sections together while returning a short table or revised passage. The context capacity does not specify a separate maximum answer length; the card's 1,024-token generation example is an illustration, not that limit.
The maker notes that an older released configuration contained a long-context error and that it has been corrected, including its relevance to derived checkpoints. Preserve the corrected release configuration when using the weights for long sessions. Medium 3.5's hybrid modes and 128B architecture should not be assigned to Medium 3.1 or a separately named draft-head checkpoint merely because their names share a family.
", "aboutText": "Mistral Medium 3.5 is Mistral AI's dense 128B model combining instruction following, reasoning and coding in one set of weights. It receives text and images and returns text. Its maker positions it for agentic and coding tasks, while the same checkpoint can produce a direct conversational answer or use its documented reasoning mode for a more involved task.\n\nUnified model and visual capabilities\n\nThe released card specifies 128 billion dense parameters and a 256k context window. Its vision encoder is trained to handle different image sizes and aspect ratios. Images can contribute evidence to a coding or document task: a screenshot of an interface, a diagram accompanying a requirement or a page containing information the model must interpret. The result can be a written explanation, code or structured fields rather than a generated image.\n\nThe maker describes multilingual support across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. It also highlights system-prompt adherence and native function calling with JSON output. Set the audience, language and response rules separately from the material being examined so that a source document's wording remains evidence for the task.\n\nReasoning settings\n\nThe card documents two reasoning-effort values per request. The value none gives an instruction-style answer without reasoning mode; high enables reasoning and is recommended for complex prompts and agentic coding. This is a mode choice within the same Medium 3.5 checkpoint, rather than a different model identity.\n\nFor high reasoning effort, the maker recommends temperature 0.7 and top-p 0.95. For none, it documents temperatures between 0.0 and 0.7 depending on the task and leaves top-p unset or at 1.0. These are release recommendations. Choose the output purpose first: a concise field extraction, a code change with an explanation or a comparison that requires several justified decisions.\n\nA practical reasoning task can supply a requirement, the implementation and the input that exposes a mismatch. Ask the model to identify the relevant path, explain the discrepancy and propose the smallest change that preserves the public interface. Request the revised code separately from the assumptions and examples. The model generates a proposal; compiling, running and applying it require the development environment connected to the agent.\n\nTools, documents and predictable edits\n\nThe maker's service card also lists structured outputs, predicted outputs, document questions, response prefixes, batch processing and agent conversations. For extraction, define the field names, types and treatment of missing values. JSON mode supplies a structured representation, while a custom schema defines a more specific shape in the connected interface. Ask for only the record when a program will parse it and a separate explanation when a person needs to inspect the interpretation.\n\nDocument question answering combines OCR-prepared text and structure with language-model understanding. For a comparison, identify the documents and the condition to evaluate, then request each finding with its source location. A useful output can separate shared requirements, disagreements and open questions. Direct image questions can focus on a chart label or interface element; preserving headings, units and image roles helps keep the answer tied to the supplied material.\n\nPredicted outputs are documented for tasks where much of the desired answer is already known, such as regenerating a code file or document with limited edits. Supply the existing content and identify the specific change, preserving the parts that should remain. This is different from asking for an entirely new artifact or continuing from a known response prefix. The service features describe submission and formatting options; they do not change the model's text-and-image input or text output.\n\nIn a tool-assisted workflow, the application supplies function descriptions and parameters, the model chooses a function and prepares arguments, and the application executes it and returns the result. A calculation tool can receive values read from a supplied image, while a lookup tool can provide the missing information for the next answer. Keep the tool result associated with the operation that produced it. Agentic reasoning relies on that environment rather than on text generation alone performing external actions.\n\nPreparing long sessions\n\nUse the 256k context for related source code, documents and conversation history, and keep the actual goal explicit. A coding session can retain the module, its caller and the requirements while asking for one defined change. A document session can keep the relevant sections together while returning a short table or revised passage. The context capacity does not specify a separate maximum answer length; the card's 1,024-token generation example is an illustration, not that limit.\n\nThe maker notes that an older released configuration contained a long-context error and that it has been corrected, including its relevance to derived checkpoints. Preserve the corrected release configuration when using the weights for long sessions. Medium 3.5's hybrid modes and 128B architecture should not be assigned to Medium 3.1 or a separately named draft-head checkpoint merely because their names share a family.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "1e2ddc15b08a462404979f902b34956b4cd75538f7b03c4599218b36e0359fb6", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "mistralai/mistral-medium-3-5:batchMistral Medium 3.5 combines instruction following, reasoning and coding in a dense 128B text-and-image model. It has a 256k maker context, per-request reasoning settings, native function calling and structured output for document and agent workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Mistral Medium 3.5 is Mistral AI's dense 128B model combining instruction following, reasoning and coding in one set of weights. It receives text and images and returns text. Its maker positions it for agentic and coding tasks, while the same checkpoint can produce a direct conversational answer or use its documented reasoning mode for a more involved task.
Unified model and visual capabilities
The released card specifies 128 billion dense parameters and a 256k context window. Its vision encoder is trained to handle different image sizes and aspect ratios. Images can contribute evidence to a coding or document task: a screenshot of an interface, a diagram accompanying a requirement or a page containing information the model must interpret. The result can be a written explanation, code or structured fields rather than a generated image.
The maker describes multilingual support across dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean and Arabic. It also highlights system-prompt adherence and native function calling with JSON output. Set the audience, language and response rules separately from the material being examined so that a source document's wording remains evidence for the task.
Reasoning settings
The card documents two reasoning-effort values per request. The value none gives an instruction-style answer without reasoning mode; high enables reasoning and is recommended for complex prompts and agentic coding. This is a mode choice within the same Medium 3.5 checkpoint, rather than a different model identity.
For high reasoning effort, the maker recommends temperature 0.7 and top-p 0.95. For none, it documents temperatures between 0.0 and 0.7 depending on the task and leaves top-p unset or at 1.0. These are release recommendations. Choose the output purpose first: a concise field extraction, a code change with an explanation or a comparison that requires several justified decisions.
A practical reasoning task can supply a requirement, the implementation and the input that exposes a mismatch. Ask the model to identify the relevant path, explain the discrepancy and propose the smallest change that preserves the public interface. Request the revised code separately from the assumptions and examples. The model generates a proposal; compiling, running and applying it require the development environment connected to the agent.
Tools, documents and predictable edits
The maker's service card also lists structured outputs, predicted outputs, document questions, response prefixes, batch processing and agent conversations. For extraction, define the field names, types and treatment of missing values. JSON mode supplies a structured representation, while a custom schema defines a more specific shape in the connected interface. Ask for only the record when a program will parse it and a separate explanation when a person needs to inspect the interpretation.
Document question answering combines OCR-prepared text and structure with language-model understanding. For a comparison, identify the documents and the condition to evaluate, then request each finding with its source location. A useful output can separate shared requirements, disagreements and open questions. Direct image questions can focus on a chart label or interface element; preserving headings, units and image roles helps keep the answer tied to the supplied material.
Predicted outputs are documented for tasks where much of the desired answer is already known, such as regenerating a code file or document with limited edits. Supply the existing content and identify the specific change, preserving the parts that should remain. This is different from asking for an entirely new artifact or continuing from a known response prefix. The service features describe submission and formatting options; they do not change the model's text-and-image input or text output.
In a tool-assisted workflow, the application supplies function descriptions and parameters, the model chooses a function and prepares arguments, and the application executes it and returns the result. A calculation tool can receive values read from a supplied image, while a lookup tool can provide the missing information for the next answer. Keep the tool result associated with the operation that produced it. Agentic reasoning relies on that environment rather than on text generation alone performing external actions.
Preparing long sessions
Use the 256k context for related source code, documents and conversation history, and keep the actual goal explicit. A coding session can retain the module, its caller and the requirements while asking for one defined change. A document session can keep the relevant sections together while returning a short table or revised passage. The context capacity does not specify a separate maximum answer length; the card's 1,024-token generation example is an illustration, not that limit.
The maker notes that an older released configuration contained a long-context error and that it has been corrected, including its relevance to derived checkpoints. Preserve the corrected release configuration when using the weights for long sessions. Medium 3.5's hybrid modes and 128B architecture should not be assigned to Medium 3.1 or a separately named draft-head checkpoint merely because their names share a family.
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-medium-3-5: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-medium-3-5:batch, mistralai/mistral-medium-3-5 | 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 $0.0015 / 1K tokens Output · Output tokens $0.0075 / 1K tokens Input · Cache read — / 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 $0.0015 / 1K tokens Output · Output tokens $0.0075 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-medium-3-5: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-medium-3-5:batch', 'mistralai/mistral-medium-3-5']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"mistralai/mistral-medium-3-5: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-medium-3-5:batch
Use the method paths shown above.