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Mistral · Open weights

Mistral Small 4 119B Thinking

Added Mar 17, 2026Open weights

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Mistral Small 4 with reasoning enabled (reasoning_effort=high). A hybrid MoE model with deep step-by-step reasoning for complex prompts, coding, and multi-step problem solving.

  • Reasoning
  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Reasoning effort
none · high
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)
997 tokens
Longer than 70% of 465 models

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TryQwen3 Next 80B A3B (Thinking)Granite 4.2 8BGPT 5.4 MiniAll 3

Public evaluations

Benchmarks

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

Intelligence Index

11.3

Better than 25% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    26.6

    Composite of the coding evaluations

    Better than 19% of 168 scored models

Agentic work

  • AutomationBench-AA

    1.0%

    Workflow automation, penalized for guardrail violations

    Better than 10% of 173 scored models

  • AutomationBench-AA tasks completed

    0.0%

    Workflows finished end to end without a guardrail violation

    Better than 0% of 173 scored models

  • AA-Briefcasev1.1

    241 Elo

    Agentic knowledge work, rated head to head

    Better than 4% of 166 scored models

  • GDPval-AAv2.1

    362 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 6% of 187 scored models

Document reasoning

  • GDP.pdf

    0.0%

    Professional reasoning over PDF documents, all-pass rate

    Better than 0% of 171 scored models

  • AA-LCRv1.1

    49.7%

    Reasoning across long documents

    Better than 27% of 309 scored models

  • MLCR-AA

    1.7%

    Long-context reasoning over medical records

    Better than 3% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    9.9%

    Expert-level questions across academic fields

    Better than 31% of 323 scored models

  • GPQA Diamond

    76.9%

    Graduate-level science questions

    Better than 40% of 291 scored models

  • IFBench

    48.2%

    Following precise output instructions

    Better than 44% of 215 scored models

Coding

  • Terminal-Benchv4.0

    0.0%

    Practical software engineering in a terminal

    Better than 0% of 171 scored models

  • SciCode

    38.8%

    Scientific computing in Python

    Better than 18% of 174 scored models

Legacy evaluations

  • Terminal-Bench Hard

    17.4%

    Earlier terminal task suite

    Better than 35% of 212 scored models

  • τ²-Bench Telecom

    41.2%

    Tool use in a customer-support simulation

    Better than 33% of 214 scored models

  • AA-LCR (unversioned)

    49.7%

    Long-context reasoning, earlier grading

    Better than 27% 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
133.7 tok/s
Faster than 74% of 251 models
Time to first token
0.8 s
Quicker than 98% of 251 models
Prompt cache hits
59.1%
Request success (24h)
94.3%

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.

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:thinking",
    "reasoning_effort": "none",
    "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:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.