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Mistral · Open weights

Mistral Small 4 119B

Added Mar 16, 2026Open weights

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Use in the app

Mistral Small 4 is a hybrid MoE model that unifies instruct, reasoning, and coding behavior in a single multimodal model. It supports text and image input, native function calling, JSON output, and per-request reasoning effort controls.

  • Reasoning
  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.40
Output
$1.40

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
16.4K
Average output (7 days)
92 tokens
Longer than 9% of 465 models

Side by side

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TryGPT OSS 20BSarvam 105BQwen3 Coder NextAll 3

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

Intelligence Index

9.0

Better than 16% of 326 scored models

3.8Catalog models by score55.1

Document reasoning

  • AA-LCRv1.1

    28.3%

    Reasoning across long documents

    Better than 12% of 309 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    3.8%

    Expert-level questions across academic fields

    Better than 5% of 323 scored models

  • GPQA Diamond

    57.1%

    Graduate-level science questions

    Better than 14% of 291 scored models

  • IFBench

    32.8%

    Following precise output instructions

    Better than 12% of 215 scored models

Legacy evaluations

  • Terminal-Bench Hard

    10.6%

    Earlier terminal task suite

    Better than 22% of 212 scored models

  • τ²-Bench Telecom

    18.4%

    Tool use in a customer-support simulation

    Better than 7% of 214 scored models

  • AA-LCR (unversioned)

    28.3%

    Long-context reasoning, earlier grading

    Better than 12% of 309 scored models

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
142.2 tok/s
Faster than 75% of 251 models
Time to first token
0.8 s
Quicker than 98% of 251 models
Prompt cache hits
16.6%
Request success (24h)
99.6%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 95.4%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/mistral-small-4-119b-2603",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="mistralai/mistral-small-4-119b-2603",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.