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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

Side by side

Mistral Small 4 119B Thinking 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 119B ThinkingMistralThis modelGranite 4.2 8BibmRemove
ProviderMistralibm
Discovery tierOpen weightsOpen weights
AddedMar 17, 2026Aug 31, 2026
Limits
Context window262.14KBest131.07K
Max output16.38K—
Average output997 tokens2.4K tokens
Pricing, USD per 1M tokens
Input$0.40$0.10Best
Output$1.40$0.15Best
Cache read—$0.05
Performance
Throughput123.6 tok/s—
Time to first token1.7 s—
Request success (24h)95.9%100.0%Best
Benchmark indexes
Intelligence Index11.3Best11.1
Coding Index26.6Best22.4
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputYesYes
Reasoning effortnone, highnone, low, high

Public evaluations

Benchmarks

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

  • Mistral Small 4 119B Thinking
  • Granite 4.2 8B

Intelligence Index

  • Mistral Small 4 119B Thinking11.3
  • Granite 4.2 8B11.1

Mistral Small 4 119B Thinking: Better than 25% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Small 4 119B Thinking: 26.6Top
    • Granite 4.2 8B: 22.4

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Small 4 119B Thinking: 1.0%
    • Granite 4.2 8B: 2.1%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Small 4 119B Thinking: 0.0%
    • Granite 4.2 8B: 0.3%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Small 4 119B Thinking: 241 Elo
    • Granite 4.2 8B: 310 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Small 4 119B Thinking: 362 Elo
    • Granite 4.2 8B: 491 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Small 4 119B Thinking: 0.0%
    • Granite 4.2 8B: 0.6%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Small 4 119B Thinking: 49.7%Top
    • Granite 4.2 8B: 45.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Small 4 119B Thinking: 1.7%
    • Granite 4.2 8B: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Small 4 119B Thinking: 9.9%Top
    • Granite 4.2 8B: 9.7%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Small 4 119B Thinking: 76.9%Top
    • Granite 4.2 8B: 63.1%

  • IFBench

    Following precise output instructions

    • Mistral Small 4 119B Thinking: 48.2%
    • Granite 4.2 8B: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Small 4 119B Thinking: 0.0%Top
    • Granite 4.2 8B: 0.0%Top

  • SciCode

    Scientific computing in Python

    • Mistral Small 4 119B Thinking: 38.8%Top
    • Granite 4.2 8B: 31.5%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Small 4 119B Thinking: 17.4%
    • Granite 4.2 8B: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Small 4 119B Thinking: 41.2%
    • Granite 4.2 8B: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Small 4 119B Thinking: 49.7%Top
    • Granite 4.2 8B: 45.0%

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
123.6 tok/s
Faster than 70% of 251 models
Time to first token
1.7 s
Quicker than 83% of 251 models
Prompt cache hits
23.6%
Request success (24h)
95.9%
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 66.6%
  • 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: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

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