Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesQwen3 235B 2507
48
Winner · 4/8 categories1-bit Bonsai 1.7B· Qwen3 235B 2507
Pick Qwen3 235B 2507 if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3 235B 2507 is clearly ahead on the aggregate, 48 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3 235B 2507's sharpest advantage is in knowledge, where it averages 63.8 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 77.5%.
Qwen3 235B 2507 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 | Qwen3 235B 2507 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 33% |
| BrowseComp | — | 40% |
| OSWorld-Verified | — | 30% |
| Coding | ||
| HumanEval | — | 31% |
| SWE-bench Verified | — | 15% |
| LiveCodeBench | — | 51.8% |
| SWE-bench Pro | — | 19% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 38% |
| OfficeQA Pro | — | 46% |
| ReasoningQwen3 235B 2507 wins | ||
| MuSR | 45.1% | 35% |
| BBH | — | 60% |
| LongBench v2 | — | 52% |
| MRCRv2 | — | 52% |
| KnowledgeQwen3 235B 2507 wins | ||
| GPQA | 20.7% | 77.5% |
| MMLU | — | 39% |
| SuperGPQA | — | 62.6% |
| MMLU-Pro | — | 83% |
| FrontierScience | — | 39% |
| SimpleQA | — | 54.3% |
| Instruction FollowingQwen3 235B 2507 wins | ||
| IFEval | 63% | 88.7% |
| Multilingual | ||
| MGSM | — | 63% |
| MMLU-ProX | — | 79.4% |
| MathematicsQwen3 235B 2507 wins | ||
| MATH-500 | 34.4% | 57% |
| AIME 2023 | — | 39% |
| AIME 2024 | — | 41% |
| AIME 2025 | — | 70.3% |
| HMMT Feb 2023 | — | 35% |
| HMMT Feb 2024 | — | 37% |
| HMMT Feb 2025 | — | 36% |
| BRUMO 2025 | — | 38% |
Qwen3 235B 2507 is ahead overall, 48 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 77.5%.
Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 63.8 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 has the edge for math in this comparison, averaging 55.7 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 has the edge for reasoning in this comparison, averaging 47.5 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 has the edge for instruction following in this comparison, averaging 88.7 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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