Mistral Small 4

Mistral Small 4
Mistral AI · Text Generation
POST /v1/chat/completions

Hybrid model unifying Instruct, Reasoning (Magistral), and Devstral families: 40% lower completion time and 3x throughput vs Small 3.

At a glance

FieldValue
Model idmistral-small-4
Input modalitiesText, Image
Output modalitiesText
Context window256K
Weight precision-
Max output tokens65,536
Featuresvision
Native inferenceNo
NewNo
Supported endpointsPOST /v1/chat/completions, POST /v1/responses, POST /v1/messages

Pricing

ChargeSpecRate
Inputper 1M prompt tokens$0.15
Outputper 1M generated tokens$0.60
Standard Web Searchper call$0.084
Premium Web Searchper call$0.140
Code Interpreterper call$0.084
Image Generationper image$0.280

Example request

$curl https://api.empiriolabs.ai/v1/chat/completions \
> -H 'Authorization: Bearer $EMPIRIOLABS_API_KEY' \
> -H 'Content-Type: application/json' \
> -d '{"model": "mistral-small-4", "messages": [{"role":"user","content":"Hello"}]}'

Parameters

ParameterTypeRequiredDefaultDescription
reasoning_enabledbooleannotrueEnable extended reasoning (maps to reasoning: high|none)
tool_web_searchbooleannotrueEnable web_search tool
web_search_tierenumno"standard"Standard or Premium web-search tier. Premium uses higher-quality sources. · Allowed: standard, premium
tool_code_interpreterbooleannotrueAllow the model to execute Python code in a sandbox to compute / analyze data.
tool_image_generationbooleannotrueAllow the model to generate images inline via the platform image-gen tool.
temperaturenumberno0.7Sampling temperature. 0 = deterministic, 2 = maximum randomness. · Range: 0 – 1.5
max_tokensnumberno4096Maximum tokens in the response. · Range: 1 – 32768
disable_formattingbooleannofalseSkip the EmpirioLabs Markdown formatting (citation [N] rewriting + References block when the web_search tool was used). The raw upstream answer with plain [N] citations is returned.

Notes

Tools (web search, code interpreter, image generation) are billed only when actually invoked.

Per-tool billing (usage.tool_usage)

When this model invokes built-in tools (web search, code interpreter, etc.) inside a single request, the response carries a normalized usage.tool_usage map alongside the token counts. The example below shows the shape — exact field names, units, and which tools appear can vary slightly per provider:

1"usage": {
2 "prompt_tokens": 123,
3 "completion_tokens": 456,
4 "cost_usd": 0.0042,
5 "tool_usage": {"web_search": 3, "code_interpreter": 1}
6}

The tool counts are already factored into cost_usd — they are surfaced for transparency so you can audit per-tool billing. The field is omitted when no tools were invoked.


Machine-readable schema: GET https://api.empiriolabs.ai/v1/models/mistral-small-4.