Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesGPT-4 Turbo
45
Winner · 4/8 categories1-bit Bonsai 1.7B· GPT-4 Turbo
Pick GPT-4 Turbo 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.
GPT-4 Turbo is clearly ahead on the aggregate, 45 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4 Turbo's sharpest advantage is in knowledge, where it averages 54.9 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 60%.
GPT-4 Turbo 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-4 Turbo |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 42% |
| BrowseComp | — | 54% |
| OSWorld-Verified | — | 41% |
| Coding | ||
| HumanEval | — | 52% |
| SWE-bench Verified | — | 5% |
| LiveCodeBench | — | 23% |
| SWE-bench Pro | — | 14% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 53% |
| OfficeQA Pro | — | 58% |
| ReasoningGPT-4 Turbo wins | ||
| MuSR | 45.1% | 56% |
| BBH | — | 75% |
| LongBench v2 | — | 62% |
| MRCRv2 | — | 62% |
| KnowledgeGPT-4 Turbo wins | ||
| GPQA | 20.7% | 60% |
| MMLU | — | 60% |
| SuperGPQA | — | 58% |
| MMLU-Pro | — | 51% |
| FrontierScience | — | 52% |
| SimpleQA | — | 58% |
| Instruction FollowingGPT-4 Turbo wins | ||
| IFEval | 63% | 80% |
| Multilingual | ||
| MMLU-ProX | — | 65% |
| MathematicsGPT-4 Turbo wins | ||
| MATH-500 | 34.4% | 71% |
| AIME 2023 | — | 60% |
| AIME 2024 | — | 62% |
| AIME 2025 | — | 61% |
| HMMT Feb 2023 | — | 56% |
| HMMT Feb 2024 | — | 58% |
| HMMT Feb 2025 | — | 57% |
| BRUMO 2025 | — | 59% |
GPT-4 Turbo is ahead overall, 45 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 60%.
GPT-4 Turbo has the edge for knowledge tasks in this comparison, averaging 54.9 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for math in this comparison, averaging 62.8 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for reasoning in this comparison, averaging 60.4 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for instruction following in this comparison, averaging 80 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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