Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesGPT-5.4
82
Winner · 4/8 categories1-bit Bonsai 8B· GPT-5.4
Pick GPT-5.4 if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.4 is clearly ahead on the aggregate, 82 to 50. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in knowledge, where it averages 83.1 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 92.8%.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 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-5.4 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. GPT-5.4 gives you the larger context window at 1.05M, 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-5.4 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 75.1% |
| BrowseComp | — | 82.7% |
| OSWorld-Verified | — | 75% |
| MCP Atlas | — | 67.2% |
| Toolathlon | — | 54.6% |
| tau2-bench | — | 98.9% |
| Coding | ||
| HumanEval | — | 95% |
| SWE-bench Verified | — | 84% |
| LiveCodeBench | — | 84% |
| SWE-bench Pro | — | 57.7% |
| React Native Evals | — | 82.6% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 81.2% |
| OfficeQA Pro | — | 96% |
| MMMU-Pro w/ Python | — | 81.5% |
| OmniDocBench 1.5 | — | 0.1090 |
| ReasoningGPT-5.4 wins | ||
| MuSR | 50% | 94% |
| BBH | — | 97% |
| MRCRv2 | — | 97% |
| MRCR v2 64K-128K | — | 86% |
| MRCR v2 128K-256K | — | 79.3% |
| Graphwalks BFS 128K | — | 93.1% |
| Graphwalks Parents 128K | — | 89.8% |
| ARC-AGI-2 | — | 73.3% |
| KnowledgeGPT-5.4 wins | ||
| GPQA | 30% | 92.8% |
| MMLU | — | 99% |
| SuperGPQA | — | 96% |
| MMLU-Pro | — | 93% |
| HLE | — | 48% |
| FrontierScience | — | 91% |
| HLE w/o tools | — | 39.8% |
| SimpleQA | — | 97% |
| Instruction FollowingGPT-5.4 wins | ||
| IFEval | 79.8% | 96% |
| Multilingual | ||
| MGSM | — | 96% |
| MMLU-ProX | — | 94% |
| MathematicsGPT-5.4 wins | ||
| MATH-500 | 66% | 99% |
| AIME 2023 | — | 99% |
| AIME 2024 | — | 99% |
| AIME 2025 | — | 99% |
| HMMT Feb 2023 | — | 96% |
| HMMT Feb 2024 | — | 98% |
| HMMT Feb 2025 | — | 97% |
| BRUMO 2025 | — | 97% |
GPT-5.4 is ahead overall, 82 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 92.8%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 83.1 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for math in this comparison, averaging 98.3 versus 66. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for reasoning in this comparison, averaging 87.7 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for instruction following in this comparison, averaging 96 versus 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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