Source: https://ailoot.ai/models/en/mistralai-mistral-medium-3-1-u003a-batch/
Canonical: https://ailoot.ai/models/en/mistralai-mistral-medium-3-1-u003a-batch/
Language: en

# Mistral: Mistral Medium 3.1 (batch)

[Markdown](https://ailoot.ai/models/en/mistralai-mistral-medium-3-1-u003a-batch/index.md) · [JSON](https://ailoot.ai/models/en/mistralai-mistral-medium-3-1-u003a-batch/facts.json)

## [Mistral: Mistral Medium 3.1 (batch)](https://ailoot.ai/models/en/mistralai-mistral-medium-3-1-u003a-batch/)

Mistral Medium 3.1 is a text-and-image model for conversational answers, document questions and structured extraction. Its 128k maker context and listed function-calling, predicted-output and prefix features support source-based explanations and targeted editing workflows.

- [**Ailoot Ultra · Batch** `POST /v1/batches` `mistralai/mistral-medium-3.1:batch`](https://ailoot.ai/models/en/mistralai-mistral-medium-3-1-u003a-batch/#method-0)

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

Mistral Medium 3.1 is a multimodal Mistral AI edition for textual and visual understanding. The maker describes this revision as improving tone and performance within the Medium 3 line. It accepts text and images and produces text: an explanation, document answer, structured record or proposed function call. The maker's fixed identifier for this edition is mistral-medium-2508.

Context and the specific edition

The model page documents a 128k context window. Use it to keep related source passages, image evidence, the conversation and the user's task together. Identify the document roles and the question to answer before supplying a large collection. A long context can support a focused comparison while the final artifact remains a short table or revised section.

Medium 3.1 retains its own specification. The 256k context, dense 128B architecture and configurable hybrid reasoning modes described for Medium 3.5 should not be assigned to this entry. The 3.1 page does not provide a parameter count or a separate maximum answer-length figure that can be safely substituted from the later model.

Structured output and functions

The maker lists structured outputs, function calling and predicted outputs among this edition's features. For a structured extraction task, define the required fields, expected types and treatment of information absent from the source. A record can contain the document identifier, the statement extracted, its source location and the question that remains. Ask for the record alone when a program will parse it, or keep a prose explanation separate when someone must inspect the interpretation.

Mistral's shared structured-output documentation distinguishes JSON mode from a custom schema. JSON mode requires an explicit instruction about the desired JSON format, while the custom route defines a more specific structure. This is formatting of the text result, not a new content modality or an external action.

For functions, the application defines the available operation and arguments, the model selects an appropriate call and prepares values, and the application executes it. Its result then becomes information for the next answer. A document assistant can use a lookup to obtain missing reference data, but only if the surrounding application supplies that lookup and returns its actual result. Keep the proposed call and the returned data associated with one another.

Predicted edits and response prefixes

Predicted outputs are useful when most of the desired result is already known, such as a document or code file needing a limited edit. Provide the existing content and describe the change precisely, including what must remain unchanged. A request might update one section's terminology or replace one implementation detail while preserving the rest of the document. The prediction contains expected output content rather than a completed change applied to a file.

The model page also lists response-prefix continuation. In the maker's chat documentation, a prefix supplies a known beginning for the assistant's response, after which generation continues. A known beginning and a predicted body solve different preparation problems. Batching and agent/conversation interfaces are likewise workflow features, rather than separate image, audio or reasoning editions of Medium 3.1.

Document and visual questions

The listed document-QnA workflow combines OCR-prepared text, structure and formatting with language-model analysis. Ask about the particular condition, passage or relationship required by the task. A comparison can identify the source documents and request shared statements, conflicts and unresolved details, retaining the source location beside each finding.

The maker's vision guide explicitly includes Medium 3.1. A direct visual question can provide a readable chart, document image or screenshot and ask what visible evidence supports a conclusion. Preserve axes, units, labels and the context around a cropped field. If several images are supplied, identify their order and role rather than leaving the model to infer which is the original, revision or reference.

For example, pair a written process requirement with a screenshot and request a table of visible matches, differences and questions requiring more information. Another task can supply two versions of a document and ask for a revised passage that preserves their shared conditions while resolving a named inconsistency. These are proposed applications of the documented visual, document and editing capabilities, with the required result defined in advance.

Preparing the final artifact

Give the assistant a clear role, the source material to use and the form of the answer. Keep durable response rules distinct from the document being examined, and state the final language and terminology. A useful response can separate an extracted fact, its source and an interpretation, making it easier to inspect than a single merged summary.

The documented result remains text. External tools, file changes and further document processing belong to the application connected to the conversation. Preserve the named Medium 3.1 edition and its 128k context, and request the explanation, structured record or targeted revision the next step actually needs.

**Ailoot Ultra · Batch** `POST /v1/batches` `mistralai/mistral-medium-3.1:batch`

[`mistralai/mistral-medium-3.1:batch`](https://ailoot.ai/models/en/#m=mistralai%2Fmistral-medium-3.1%3Abatch&route=Ultra&caller=mistralai%2Fmistral-medium-3.1%3Abatch&fmt=batches)

Mistral /`mistralai/mistral-medium-3.1:batch`

### Overview

Mistral Medium 3.1 is a text-and-image model for conversational answers, document questions and structured extraction. Its 128k maker context and listed function-calling, predicted-output and prefix features support source-based explanations and targeted editing workflows.

Accepted inputs

Text

Results

Text

### Available in Ailoot · inputs & methods

**Ailoot Ultra · Batch** `POST /v1/batches`

Input types

`text`

Output types

`text`

Output format

inline results\[\].response.body JSON

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`endpoint` | Type`string` | RequirementRequired | Values & limits /v1/chat/completions | Default— |
| Field`model` | Type`string` | RequirementRequired | Values & limits mistralai/mistral-medium-3.1:batch | Default— |
| Field`requests` | Type`batch_item[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— |
| Field`requests[].custom_id` | Type`string` | RequirementRequired | Values & limits | Default— |
| Field`requests[].body.messages` | Type`message[]` | RequirementRequired | Values & limits | Default— |
| Field`completion_window` | Type`string` | RequirementOptional | Values & limits 24h | Default— |
| Field`provider` | Type`object\|null` | RequirementOptional | Values & limits | Default— |
| Field`requests[].body.model` | Type`string` | RequirementOptional | Values & limits mistralai/mistral-medium-3.1:batch, mistralai/mistral-medium-3.1 | Default— |
| Field`provider.only` | Type`string[]` | RequirementOptional | Values & limits mistral | Default— |
| Field`provider.allow_fallbacks` | Type`boolean\|null` | RequirementOptional | Values & limits false, null | Default— |

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractendpoint, model, requests |
| Parameter`material_types` | Contracttext |
| Parameter`representation` | Contractinline requests\[\]; each item has custom\_id and body |
| Parameter`request_endpoint_profile` | Contract/v1/chat/completions |
| Parameter`file_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. |
| Parameter`serialization_order` | Contractendpoint, model, provider (if supplied), completion\_window (if supplied), requests last |
| Parameter`optional_controls` | Contractprovider.only, provider.allow\_fallbacks, completion\_window |
| Parameter`schema_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

### Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode

Not specified When fast mode is selected

Flex mode

Not specified When flex mode is selected

Maximum reasoning effort

Not specified When maximum reasoning effort uses the default adjustment

Explicit cache pricing

Not enabled When explicit cache is requested

#### Ailoot rates

#### Prices by configuration

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 **$0.0004** / 1K tokens Output · Output tokens **$0.002** / 1K tokens Input · Cache read **$0.00004** / 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 **$0.0004** / 1K tokens Output · Output tokens **$0.002** / 1K tokens Input · Cache read **$0.00004** / 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 →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · Batch

curl · retained fixturePython HTTP · actual customer JSONLPython · save every completed result

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json
from pathlib import Path
body=json.loads('{"endpoint":"/v1/chat/completions","model":"mistralai/mistral-medium-3.1:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')
Path("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json
```

```python
import json, os
from pathlib import Path
from urllib.request import Request, urlopen

def prepare_requests(customer_path):
    path = Path(customer_path)
    if not path.is_file(): raise ValueError("Customer JSONL file is missing")
    items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
    if not items: raise ValueError("Customer JSONL contains no requests")
    ids = set()
    for item in items:
        if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")
        cid = item["custom_id"]
        if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")
        ids.add(cid)
        if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")
        supplied_model = item["body"].get("model")
        if supplied_model is not None and supplied_model not in ['mistralai/mistral-medium-3.1:batch', 'mistralai/mistral-medium-3.1']: raise ValueError("body.model must be an exact documented caller or wire ID")
    return {"endpoint":"/v1/chat/completions", "model":"mistralai/mistral-medium-3.1:batch", "requests":items}

if __name__ == "__main__":
    material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")
    if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")
    body = prepare_requests(material)
    key = os.environ.get("AILOOT_API_KEY", "")
    if not key: raise SystemExit("Set AILOOT_API_KEY")
    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")
    with urlopen(request) as response:
        raw = response.read()
        Path("batch-create-response.json").write_bytes(raw)
        print(raw.decode("utf-8"))
```

```python
import json
from pathlib import Path
raw = Path("batch-status-response.json").read_bytes()
response = json.loads(raw)
if response.get("status") != "completed":
    raise SystemExit("Keep the original operation; read its status without resubmitting")
results = response.get("results")
if not isinstance(results, list) or not results:
    raise SystemExit("Preserve the original response; completed results are missing")
Path("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\n" for item in results), encoding="utf-8")
# Preserve batch-status-response.json with aggregate usage and every result/error.
```

### Receive and save results

#### Ailoot Ultra · Batch

inline results\[\].response.body JSON

- `GET /v1/batches/{operation_id}`

##### Check job status

curl · original operation status

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
BATCH_OPERATION_ID="$(python3 - <<'PY_ID'
import json
from pathlib import Path
from urllib.parse import quote
response = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))
operation = response.get("id")
if not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")
print(quote(operation, safe=""))
PY_ID
)"
curl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \
  -H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json
```

##### Keep all completed results

Keep 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.

##### If the original response needs checking

Preserve the original operation ID. 503 result\_pending\_check has X-Should-Retry:false; never resubmit an unknown original batch.

Response fields

| Field | Type |
| --- | --- |
| Field`results[].custom_id` | Typestring |
| Field`results[].response.body.choices[].message.content` | Typestring\|null |
| Field`results[].error` | Typeobject\|null |
| Field`usage` | Typeobject\|null |
| Field`id` | Typestring |
| Field`model` | Typestring |
| Field`endpoint` | Typestring |
| Field`status` | Typestring |
| Field`request_counts` | Typeobject |
| Field`results[].id` | Typestring |
| Field`results[].response.status_code` | Typeinteger |
| Field`results[].response.request_id` | Typestring |

Errors and next steps

| 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. |

### Aliases

#### Model aliases

**Ailoot Ultra** [`mistralai/mistral-medium-3.1:batch`](https://ailoot.ai/models/en/#m=mistralai%2Fmistral-medium-3.1%3Abatch&route=Ultra&caller=mistralai%2Fmistral-medium-3.1%3Abatch&fmt=batches)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`mistralai/mistral-medium-3.1:batch`](https://ailoot.ai/models/en/#m=mistralai%2Fmistral-medium-3.1%3Abatch&route=Ultra&caller=mistralai%2Fmistral-medium-3.1%3Abatch&fmt=batches)

FAQ · Mistral: Mistral Medium 3.1 (batch)

What inputs and results are available?

`POST /v1/batches` · `mistralai/mistral-medium-3.1:batch`: `text` → `text`

Which fields does the request use?

`POST /v1/batches` · `mistralai/mistral-medium-3.1:batch`: `endpoint`, `model`, `requests`, `completion_window`, `provider`

What limits apply?

`POST /v1/batches` · `mistralai/mistral-medium-3.1:batch`: `requests.min_items`: `1`

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