Mistral AI, released Mar 17, 2025

Mistral Small 3.1 24Bprice, context, benchmarks and release details

Provisional: not enough results to rank yet Open weights 64% confidence 64 percent, Medium confidence, 2 of 7 expected sources in
48.4
SI Score
#111 of 142 ranked models
Input, per 1M tokens
not yet reported
Output, per 1M tokens
not yet reported
Context window
128Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Max output
16.4Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Released
Mar 17, 2025models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗

How the score breaks down

Coding (weight 40 percent) —
Math (weight 15 percent) 33.1
Preference (weight 15 percent) 59.5
Reasoning (weight 30 percent) 47.5

Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 05:13 UTC.

Around it on the leaderboard

  1. 109 GPT-4o (2024-05-13) 49.4
  2. 110 Claude Sonnet 3.7 48.9
  3. 111 Mistral Small 3.1 24B 48.4
  4. 112 QwQ 32B 48.2
  5. 113 DeepSeek-R1 47.9

Full leaderboard

Benchmark results

4 benchmarks, 4 results

Each row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.

GPQA Diamondreasoning 47.5%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Mar 18, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
47.5
MATH Level 5math 46.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Mar 18, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
46.8
OTIS Mock AIME 2024–2025math 5.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Mar 18, 2025 Retrieved Oct 9, 2026 · CC-BY
Open source ↗
5.8
LMArena Textpreference 1277.3 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 8, 2026 Retrieved Oct 9, 2026 · CC-BY-4.0
Open source ↗
59.5

Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.

Details and sources

Open weights
Yesmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
License
not yet reported
Input modalities
text, imagemodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
First seen by SuperIndex
Oct 8, 2026
Coverage
51% of expected source weight

Reported (2)

  • Epoch AI BenchmarkingOct 9, 2026
  • LMArena / ArenaOct 8, 2026

Awaiting (5)

  • ARC Prize13% of weight
  • Humanity’s Last Exam13% of weight
  • Official model cards via models.dev4% of weight
  • LiveBench13% of weight
  • Terminal-Bench6% of weight

Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.

What changed

All releases · RSS feed

Alerts on this device

What to be alerted about
RSS feed