Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
1/8 categoriesQwen3 235B 2507 (Reasoning)
55
Winner · 3/8 categories1-bit Bonsai 8B· Qwen3 235B 2507 (Reasoning)
Pick Qwen3 235B 2507 (Reasoning) if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if mathematics is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3 235B 2507 (Reasoning) is clearly ahead on the aggregate, 55 to 50. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3 235B 2507 (Reasoning)'s sharpest advantage is in knowledge, where it averages 50 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 81.1%. 1-bit Bonsai 8B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while 1-bit Bonsai 8B 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. Qwen3 235B 2507 (Reasoning) 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 | Qwen3 235B 2507 (Reasoning) |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 47% |
| BrowseComp | — | 48% |
| OSWorld-Verified | — | 43% |
| Coding | ||
| HumanEval | — | 32% |
| SWE-bench Verified | — | 16% |
| LiveCodeBench | — | 74.1% |
| SWE-bench Pro | — | 29% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 38% |
| OfficeQA Pro | — | 47% |
| ReasoningQwen3 235B 2507 (Reasoning) wins | ||
| MuSR | 50% | 36% |
| BBH | — | 63% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 58% |
| KnowledgeQwen3 235B 2507 (Reasoning) wins | ||
| GPQA | 30% | 81.1% |
| MMLU | — | 40% |
| SuperGPQA | — | 64.9% |
| MMLU-Pro | — | 84.4% |
| HLE | — | 6% |
| FrontierScience | — | 42% |
| SimpleQA | — | 38% |
| Instruction FollowingQwen3 235B 2507 (Reasoning) wins | ||
| IFEval | 79.8% | 87.8% |
| Multilingual | ||
| MGSM | — | 62% |
| MMLU-ProX | — | 81% |
| Mathematics1-bit Bonsai 8B wins | ||
| MATH-500 | 66% | 60% |
| AIME 2023 | — | 40% |
| AIME 2024 | — | 42% |
| AIME 2025 | — | 92.3% |
| HMMT Feb 2023 | — | 36% |
| HMMT Feb 2024 | — | 38% |
| HMMT Feb 2025 | — | 37% |
| BRUMO 2025 | — | 39% |
Qwen3 235B 2507 (Reasoning) is ahead overall, 55 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 81.1%.
Qwen3 235B 2507 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 50 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
1-bit Bonsai 8B has the edge for math in this comparison, averaging 66 versus 65.6. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for reasoning in this comparison, averaging 52.1 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for instruction following in this comparison, averaging 87.8 versus 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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