Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesMistral Small 4 (Reasoning)
~64
Winner · 2/8 categories1-bit Bonsai 4B· Mistral Small 4 (Reasoning)
Pick Mistral Small 4 (Reasoning) if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Mistral Small 4 (Reasoning) is clearly ahead on the aggregate, 64 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mistral Small 4 (Reasoning)'s sharpest advantage is in knowledge, where it averages 75.6 against 28.7. The single biggest benchmark swing on the page is GPQA, 28.7% to 71.2%.
Mistral Small 4 (Reasoning) is the reasoning model in the pair, while 1-bit Bonsai 4B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Mistral Small 4 (Reasoning) gives you the larger context window at 256K, compared with 32K for 1-bit Bonsai 4B.
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 Small 4 (Reasoning) |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | — | 63.6% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 60% |
| Reasoning | ||
| MuSR | 41.4% | — |
| KnowledgeMistral Small 4 (Reasoning) wins | ||
| GPQA | 28.7% | 71.2% |
| MMLU-Pro | — | 78% |
| Instruction Following | ||
| IFEval | 69.6% | — |
| Multilingual | ||
| Coming soon | ||
| MathematicsMistral Small 4 (Reasoning) wins | ||
| MATH-500 | 65.8% | — |
| AIME 2025 | — | 83.8% |
Mistral Small 4 (Reasoning) is ahead overall, 64 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 71.2%.
Mistral Small 4 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 75.6 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Mistral Small 4 (Reasoning) has the edge for math in this comparison, averaging 83.8 versus 65.8. 1-bit Bonsai 4B stays close enough that the answer can still flip depending on your workload.
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