Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesMistral 8x7B
44
4/8 categories1-bit Bonsai 4B· Mistral 8x7B
Treat this as a split decision. 1-bit Bonsai 4B makes more sense if its workflow fits your team better; Mistral 8x7B is the better fit if knowledge is the priority.
1-bit Bonsai 4B and Mistral 8x7B finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
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 4B | Mistral 8x7B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 40% |
| BrowseComp | — | 47% |
| OSWorld-Verified | — | 38% |
| Coding | ||
| HumanEval | — | 32.3% |
| SWE-bench Verified | — | 28% |
| LiveCodeBench | — | 23% |
| SWE-bench Pro | — | 28% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 42% |
| OfficeQA Pro | — | 56% |
| ReasoningMistral 8x7B wins | ||
| MuSR | 41.4% | 61% |
| BBH | — | 67.1% |
| LongBench v2 | — | 57% |
| MRCRv2 | — | 53% |
| KnowledgeMistral 8x7B wins | ||
| GPQA | 28.7% | 64% |
| MMLU | — | 71.3% |
| SuperGPQA | — | 62% |
| MMLU-Pro | — | 65% |
| HLE | — | 8% |
| FrontierScience | — | 56% |
| SimpleQA | — | 63% |
| Instruction FollowingMistral 8x7B wins | ||
| IFEval | 69.6% | 78% |
| Multilingual | ||
| MGSM | — | 74% |
| MMLU-ProX | — | 71% |
| MathematicsMistral 8x7B wins | ||
| MATH-500 | 65.8% | 73% |
| AIME 2023 | — | 65% |
| AIME 2024 | — | 67% |
| AIME 2025 | — | 66% |
| HMMT Feb 2023 | — | 61% |
| HMMT Feb 2024 | — | 63% |
| HMMT Feb 2025 | — | 62% |
| BRUMO 2025 | — | 64% |
1-bit Bonsai 4B and Mistral 8x7B are tied on overall score, so the right pick depends on which category matters most for your use case.
Mistral 8x7B has the edge for knowledge tasks in this comparison, averaging 49.5 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Mistral 8x7B has the edge for math in this comparison, averaging 67.1 versus 65.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Mistral 8x7B has the edge for reasoning in this comparison, averaging 56.7 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Mistral 8x7B has the edge for instruction following in this comparison, averaging 78 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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