Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesGPT-5 (high)
82
Winner · 4/8 categories1-bit Bonsai 1.7B· GPT-5 (high)
Pick GPT-5 (high) if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5 (high) is clearly ahead on the aggregate, 82 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 (high)'s sharpest advantage is in mathematics, where it averages 94.8 against 34.4. The single biggest benchmark swing on the page is GPQA, 20.7% to 91%.
GPT-5 (high) is the reasoning model in the pair, while 1-bit Bonsai 1.7B 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. GPT-5 (high) 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 | GPT-5 (high) |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 78% |
| BrowseComp | — | 75% |
| OSWorld-Verified | — | 72% |
| Coding | ||
| HumanEval | — | 85% |
| SWE-bench Verified | — | 67% |
| LiveCodeBench | — | 62% |
| SWE-bench Pro | — | 70% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 93% |
| OfficeQA Pro | — | 85% |
| ReasoningGPT-5 (high) wins | ||
| MuSR | 45.1% | 87% |
| BBH | — | 94% |
| LongBench v2 | — | 83% |
| MRCRv2 | — | 80% |
| KnowledgeGPT-5 (high) wins | ||
| GPQA | 20.7% | 91% |
| MMLU | — | 93% |
| SuperGPQA | — | 89% |
| MMLU-Pro | — | 83% |
| HLE | — | 27% |
| FrontierScience | — | 83% |
| SimpleQA | — | 89% |
| Instruction FollowingGPT-5 (high) wins | ||
| IFEval | 63% | 91% |
| Multilingual | ||
| MGSM | — | 89% |
| MMLU-ProX | — | 85% |
| MathematicsGPT-5 (high) wins | ||
| MATH-500 | 34.4% | 94% |
| AIME 2023 | — | 95% |
| AIME 2024 | — | 97% |
| AIME 2025 | — | 96% |
| HMMT Feb 2023 | — | 91% |
| HMMT Feb 2024 | — | 93% |
| HMMT Feb 2025 | — | 92% |
| BRUMO 2025 | — | 94% |
GPT-5 (high) is ahead overall, 82 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 91%.
GPT-5 (high) has the edge for knowledge tasks in this comparison, averaging 72.6 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for math in this comparison, averaging 94.8 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for reasoning in this comparison, averaging 83.1 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GPT-5 (high) has the edge for instruction following in this comparison, averaging 91 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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