Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesGPT-4o
51
Winner · 4/8 categories1-bit Bonsai 8B· GPT-4o
Pick GPT-4o if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if you want the cheaper token bill.
GPT-4o finishes one point ahead overall, 51 to 50. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
GPT-4o's sharpest advantage is in knowledge, where it averages 48.7 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 66%.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 8B. That is roughly Infinityx on output cost alone. GPT-4o 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 | GPT-4o |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 49% |
| BrowseComp | — | 59% |
| OSWorld-Verified | — | 48% |
| Coding | ||
| HumanEval | — | 58% |
| SWE-bench Verified | — | 20% |
| LiveCodeBench | — | 38% |
| SWE-bench Pro | — | 29% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 74% |
| OfficeQA Pro | — | 70% |
| ReasoningGPT-4o wins | ||
| MuSR | 50% | 62% |
| BBH | — | 82% |
| LongBench v2 | — | 62% |
| MRCRv2 | — | 63% |
| KnowledgeGPT-4o wins | ||
| GPQA | 30% | 66% |
| MMLU | — | 66% |
| SuperGPQA | — | 64% |
| MMLU-Pro | — | 64% |
| HLE | — | 1% |
| FrontierScience | — | 58% |
| SimpleQA | — | 64% |
| Instruction FollowingGPT-4o wins | ||
| IFEval | 79.8% | 82% |
| Multilingual | ||
| MGSM | — | 82% |
| MMLU-ProX | — | 72% |
| MathematicsGPT-4o wins | ||
| MATH-500 | 66% | 80% |
| AIME 2023 | — | 66% |
| AIME 2024 | — | 68% |
| AIME 2025 | — | 67% |
| HMMT Feb 2023 | — | 62% |
| HMMT Feb 2024 | — | 64% |
| HMMT Feb 2025 | — | 63% |
| BRUMO 2025 | — | 65% |
GPT-4o is ahead overall, 51 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 66%.
GPT-4o has the edge for knowledge tasks in this comparison, averaging 48.7 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4o has the edge for math in this comparison, averaging 69.6 versus 66. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-4o has the edge for reasoning in this comparison, averaging 62.3 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GPT-4o has the edge for instruction following in this comparison, averaging 82 versus 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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