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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 468 models

Side by side

Mistral Small 4 119B compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Mistral Small 4 119BMistralThis modelMistral Small 3 24B (2501)MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedMar 16, 2026Sep 10, 2026
Limits
Context window262.14KBest32.77K
Max output16.38KBest8.19K
Average output92 tokens403 tokens
Pricing, USD per 1M tokens
Input$0.40$0.12Best
Output$1.40$0.35Best
Performance
Throughput128.8 tok/s—
Time to first token0.7 s—
Request success (24h)99.6%—
Benchmark indexes
Intelligence Index9.0Best6.7
Math Index—4.3
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesNo
Structured outputYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Mistral Small 4 119B
  • Mistral Small 3 24B (2501)

Intelligence Index

  • Mistral Small 4 119B9.0
  • Mistral Small 3 24B (2501)6.7

Mistral Small 4 119B: Better than 16% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Mistral Small 4 119B: —
    • Mistral Small 3 24B (2501): 4.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Small 4 119B: 28.3%Top
    • Mistral Small 3 24B (2501): 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Small 4 119B: 3.8%Top
    • Mistral Small 3 24B (2501): 3.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Small 4 119B: 57.1%Top
    • Mistral Small 3 24B (2501): 46.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Mistral Small 4 119B: —
    • Mistral Small 3 24B (2501): 65.2%

  • IFBench

    Following precise output instructions

    • Mistral Small 4 119B: 32.8%Top
    • Mistral Small 3 24B (2501): 26.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Mistral Small 4 119B: —
    • Mistral Small 3 24B (2501): 25.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Mistral Small 4 119B: —
    • Mistral Small 3 24B (2501): 4.3%

  • AIME

    Competition mathematics

    • Mistral Small 4 119B: —
    • Mistral Small 3 24B (2501): 8.0%

  • MATH-500

    Competition math problems

    • Mistral Small 4 119B: —
    • Mistral Small 3 24B (2501): 71.5%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Small 4 119B: 10.6%
    • Mistral Small 3 24B (2501): —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Small 4 119B: 18.4%
    • Mistral Small 3 24B (2501): 19.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Small 4 119B: 28.3%Top
    • Mistral Small 3 24B (2501): 0.0%

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
128.8 tok/s
Faster than 70% of 254 models
Time to first token
0.7 s
Quicker than 99% of 254 models
Prompt cache hits
16.9%
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.