Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesMistral Large 2
52
Winner · 4/8 categories1-bit Bonsai 8B· Mistral Large 2
Pick Mistral Large 2 if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Mistral Large 2 has the cleaner overall profile here, landing at 52 versus 50. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Mistral Large 2's sharpest advantage is in knowledge, where it averages 55.4 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 68%.
Mistral Large 2 gives you the larger context window at 128K, compared with 64K for 1-bit Bonsai 8B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | 1-bit Bonsai 8B | Mistral Large 2 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 51% |
| BrowseComp | — | 57% |
| OSWorld-Verified | — | 50% |
| Coding | ||
| HumanEval | — | 60% |
| SWE-bench Verified | — | 49% |
| LiveCodeBench | — | 38% |
| SWE-bench Pro | — | 44% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 56% |
| OfficeQA Pro | — | 67% |
| ReasoningMistral Large 2 wins | ||
| MuSR | 50% | 64% |
| BBH | — | 82% |
| LongBench v2 | — | 66% |
| MRCRv2 | — | 68% |
| KnowledgeMistral Large 2 wins | ||
| GPQA | 30% | 68% |
| MMLU | — | 68% |
| SuperGPQA | — | 66% |
| MMLU-Pro | — | 74% |
| HLE | — | 12% |
| FrontierScience | — | 65% |
| SimpleQA | — | 66% |
| Instruction FollowingMistral Large 2 wins | ||
| IFEval | 79.8% | 83% |
| Multilingual | ||
| MGSM | — | 81% |
| MMLU-ProX | — | 78% |
| MathematicsMistral Large 2 wins | ||
| MATH-500 | 66% | 82% |
| AIME 2023 | — | 68% |
| AIME 2024 | — | 70% |
| AIME 2025 | — | 69% |
| HMMT Feb 2023 | — | 64% |
| HMMT Feb 2024 | — | 66% |
| HMMT Feb 2025 | — | 65% |
| BRUMO 2025 | — | 67% |
Mistral Large 2 is ahead overall, 52 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 68%.
Mistral Large 2 has the edge for knowledge tasks in this comparison, averaging 55.4 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Mistral Large 2 has the edge for math in this comparison, averaging 71.6 versus 66. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Mistral Large 2 has the edge for reasoning in this comparison, averaging 66.1 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Mistral Large 2 has the edge for instruction following in this comparison, averaging 83 versus 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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