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Mistral · Open weights

Mistral Small 3.1 24B (2503)

Added Apr 15, 2025Open weights

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Use in the app

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.

  • Vision
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.30

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
102.4K

Side by side

Mistral Small 3.1 24B (2503) compared

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

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Mistral Small 3.1 24B (2503)MistralThis modelGLM 5.3 FlashZ.aiRemove
ProviderMistralZ.ai
Discovery tierOpen weightsOpen weights
AddedApr 15, 2025Aug 26, 2026
Limits
Context window128K1.05MBest
Max output102.4K131.07KBest
Average output—1.3K tokens
Pricing, USD per 1M tokens
Input$0.10$0.10
Output$0.30$0.30
Cache read—$0.025
Performance
Throughput—146.9 tok/s
Time to first token—2.7 s
Request success (24h)99.6%99.6%
Benchmark indexes
Intelligence Index—41.8
Coding Index—71.5
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
Video inputNoYes
Reasoning effort—low, high, max

Public evaluations

Benchmarks

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

  • Mistral Small 3.1 24B (2503)
  • GLM 5.3 Flash

Intelligence Index

  • Mistral Small 3.1 24B (2503)—
  • GLM 5.3 Flash41.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 71.5

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 60.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 25.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 0.8%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 1,452 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 1,642 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 15.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 80.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 11.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 39.9%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 91.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 32.8%

  • SciCode

    Scientific computing in Python

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 51.6%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Small 3.1 24B (2503): —
    • GLM 5.3 Flash: 80.0%

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.

Prompt cache hits
0.0%
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 96.0%
  • 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-3.1-24b-instruct",
    "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-3.1-24b-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

Other Mistral models, starting with the same family.