Mistral Large 2.1: Price, Context, Benchmarks, and Release Details
37.4
SI Score (method si-v2-absolute-shrinkage-1)
#111 of 115 ranked
88% confidence 88 percent, High confidence — 2 of 3 expected sources in
Coverage 70% of expected source weight · 100% confidence at 80% coverage
Pillar breakdown
Coding (weight 40 percent) 6.1
Math (weight 15 percent) 36.1
Preference (weight 15 percent) 58.1
Reasoning (weight 30 percent) 51.3
Pillar weights: reasoning 30% · math 15% · coding 40% · preference 15%. Benchmark results use fixed 0–100 scales before averaging; incomplete evidence is shrunk toward 50.
Facts
- Input price / 1M
- not yet reported
- Output price / 1M
- not yet reported
- Context window
- 131Kmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Max output
- 16.4Kmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Released
- Nov 18, 2024models.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - Open weights
- Yesmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗ - License
- not yet reported
- Input modalities
- textmodels.devPublished source fact
Retrieved Oct 8, 2026 · MIT
Open source ↗
Benchmark results
5 results| Benchmark | Raw result | Normalized (0–100) | Pillar |
|---|---|---|---|
| gpqa diamond | 51.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 25, 2025
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 51.3 | reasoning |
| lmarena text | 1265.3 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 2, 2026
Retrieved Oct 8, 2026 · CC-BY-4.0 Open source ↗ | 58.1 | preference |
| math level 5 | 50.3%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 25, 2025
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 50.3 | math |
| otis mock aime 2024 2025 | 7.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] Published Feb 25, 2025
Retrieved Oct 8, 2026 · CC-BY Open source ↗ | 7.8 | math |
| terminal bench | 6.1%Official model cards via models.devLab-reported; metric success rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Published Mar 11, 2026
Retrieved Oct 8, 2026 · factual citation; MIT transcription Open source ↗ | 6.1 | coding |
Normalization uses fixed absolute 0–100 scales for each unit, independently of other models that have a result on each benchmark. Coverage and evidence breadth still affect the composite; compare the evaluation conditions before reading a small score gap as decisive. “lab-reported” marks the provider's own published figure.
Sources: in and pending
in Reported (2)
- Official model cards via models.dev · arrived Oct 8, 2026
- LMArena / Arena · arrived Oct 8, 2026
pending Awaiting (1)
- LiveBench · carries 30% of expected weight
The confidence % rises as pending sources publish. Some sources never cover some models — that is why 100% confidence arrives at 80% of expected weight, not at full coverage.