Mistral AI, released Jul 10, 2025
Devstral Smallprice, context, benchmarks and release details
- 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
- 128Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- Jul 10, 2025models.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗
How the score breaks down
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, 06:15 UTC.
Around it on the leaderboard
- 1 Claude Fable 5.1 80.2
- 2 Claude Opus 5.5 78.4
- 3 GPT-6 Astra 77.5
- 4 Claude Fable 5 76.8
- 5 Claude Opus 5 74.9
Benchmark results
1 benchmarks, 2 resultsEach 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.
SWE-bench Verifiedcoding 53.6%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published Jul 10, 2025
Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
53.6 2 settings
- Setting 1 53.6%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published Jul 10, 2025
Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗ - Setting 2 38%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] SWE-agentPublished Jul 25, 2025
Retrieved Oct 9, 2026 · factual citation
Open source ↗
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
- textmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 15% of expected source weight
Reported (2)
- Official model cards via models.devOct 8, 2026
- SWE-bench VerifiedOct 8, 2026
Awaiting (6)
- ARC Prize11% of weight
- Epoch AI Benchmarking23% of weight
- Humanity’s Last Exam11% of weight
- LiveBench11% of weight
- LMArena / Arena23% 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.