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Mistral · Open weights

MS3.2 24B Magnum Diamond

Added Nov 24, 2025Open weights

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Use in the app

rsLoRA finetune of a text-only conversion of Mistral Small 3.2 24B Instruct (2506), built to bring the Magnum mix's Claude-like prose to a smaller footprint. The "Diamond" moniker nods to the extra heat and pressure - pre-tokenization plus custom loss masking - to turn the assistant-tuned base into creative-writing gems, with or without character names or prefill.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.49
Output
$0.49

Reference prices as published by the model provider. API example

Specifications

Context window
32.8K
Max output
—
Average output (7 days)
97 tokens
Longer than 9% of 468 models

Side by side

MS3.2 24B Magnum Diamond compared

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

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MS3.2 24B Magnum DiamondMistralThis modelMistral Small 3 24B (2501)MistralRemove
ProviderMistralMistral
Discovery tierOpen weightsOpen weights
AddedNov 24, 2025Sep 10, 2026
Limits
Context window32.77K32.77K
Max output—8.19K
Average output97 tokens403 tokens
Pricing, USD per 1M tokens
Input$0.49$0.12Best
Output$0.49$0.35Best
Performance
Throughput17.3 tok/s—
Time to first token19.8 s—
Request success (24h)59.3%—
Benchmark indexes
Intelligence Index—6.7
Math Index—4.3
Capabilities
ToolsNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • MS3.2 24B Magnum Diamond
  • Mistral Small 3 24B (2501)

Intelligence Index

  • MS3.2 24B Magnum Diamond—
  • Mistral Small 3 24B (2501)6.7
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 4.3

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 3.8%

  • GPQA Diamond

    Graduate-level science questions

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 46.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 65.2%

  • IFBench

    Following precise output instructions

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 26.4%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 25.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 4.3%

  • AIME

    Competition mathematics

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 8.0%

  • MATH-500

    Competition math problems

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 71.5%

Legacy evaluations

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • MS3.2 24B Magnum Diamond: —
    • Mistral Small 3 24B (2501): 19.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • MS3.2 24B Magnum Diamond: —
    • 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
17.3 tok/s
Faster than 9% of 253 models
Time to first token
19.8 s
Quicker than 1% of 253 models
Request success (24h)
59.3%

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": "Doctor-Shotgun/MS3.2-24B-Magnum-Diamond",
    "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="Doctor-Shotgun/MS3.2-24B-Magnum-Diamond",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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