Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesMistral Large 3
58
Winner · 4/8 categories1-bit Bonsai 1.7B· Mistral Large 3
Pick Mistral Large 3 if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if you want the cheaper token bill.
Mistral Large 3 is clearly ahead on the aggregate, 58 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mistral Large 3's sharpest advantage is in mathematics, where it averages 80.4 against 34.4. The single biggest benchmark swing on the page is MATH-500, 34.4% to 93.6%.
Mistral Large 3 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 1.7B. That is roughly Infinityx on output cost alone. Mistral Large 3 gives you the larger context window at 128K, compared with 32K for 1-bit Bonsai 1.7B.
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 1.7B | Mistral Large 3 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 52% |
| BrowseComp | — | 58% |
| OSWorld-Verified | — | 49% |
| Coding | ||
| HumanEval | — | 92.3% |
| SWE-bench Verified | — | 45% |
| LiveCodeBench | — | 39% |
| SWE-bench Pro | — | 42% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 75% |
| OfficeQA Pro | — | 76% |
| ReasoningMistral Large 3 wins | ||
| MuSR | 45.1% | 71% |
| BBH | — | 81% |
| LongBench v2 | — | 67% |
| MRCRv2 | — | 67% |
| KnowledgeMistral Large 3 wins | ||
| GPQA | 20.7% | 43.9% |
| MMLU | — | 85.5% |
| SuperGPQA | — | 73% |
| MMLU-Pro | — | 73.1% |
| HLE | — | 12% |
| FrontierScience | — | 67% |
| SimpleQA | — | 24% |
| Instruction FollowingMistral Large 3 wins | ||
| IFEval | 63% | 89.4% |
| Multilingual | ||
| MGSM | — | 82% |
| MMLU-ProX | — | 77% |
| MathematicsMistral Large 3 wins | ||
| MATH-500 | 34.4% | 93.6% |
| AIME 2023 | — | 76% |
| AIME 2024 | — | 78% |
| AIME 2025 | — | 77% |
| HMMT Feb 2023 | — | 72% |
| HMMT Feb 2024 | — | 74% |
| HMMT Feb 2025 | — | 73% |
| BRUMO 2025 | — | 75% |
Mistral Large 3 is ahead overall, 58 to 39. The biggest single separator in this matchup is MATH-500, where the scores are 34.4% and 93.6%.
Mistral Large 3 has the edge for knowledge tasks in this comparison, averaging 48.1 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for math in this comparison, averaging 80.4 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for reasoning in this comparison, averaging 68.1 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Mistral Large 3 has the edge for instruction following in this comparison, averaging 89.4 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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