Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
1/8 categoriesGemini 2.5 Flash
49
Winner · 3/8 categories1-bit Bonsai 4B· Gemini 2.5 Flash
Pick Gemini 2.5 Flash if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Gemini 2.5 Flash is clearly ahead on the aggregate, 49 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 2.5 Flash's sharpest advantage is in reasoning, where it averages 62.1 against 41.4. The single biggest benchmark swing on the page is GPQA, 28.7% to 49%. 1-bit Bonsai 4B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Gemini 2.5 Flash is also the more expensive model on tokens at $0.15 input / $0.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 4B. That is roughly Infinityx on output cost alone. Gemini 2.5 Flash gives you the larger context window at 1M, compared with 32K for 1-bit Bonsai 4B.
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 4B | Gemini 2.5 Flash |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 44% |
| BrowseComp | — | 58% |
| OSWorld-Verified | — | 41% |
| Coding | ||
| HumanEval | — | 42% |
| SWE-bench Verified | — | 23% |
| LiveCodeBench | — | 18% |
| SWE-bench Pro | — | 25% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 69% |
| OfficeQA Pro | — | 66% |
| ReasoningGemini 2.5 Flash wins | ||
| MuSR | 41.4% | 46% |
| BBH | — | 75% |
| LongBench v2 | — | 68% |
| MRCRv2 | — | 68% |
| KnowledgeGemini 2.5 Flash wins | ||
| GPQA | 28.7% | 49% |
| MMLU | — | 50% |
| SuperGPQA | — | 47% |
| MMLU-Pro | — | 64% |
| HLE | — | 1% |
| FrontierScience | — | 49% |
| SimpleQA | — | 48% |
| Instruction FollowingGemini 2.5 Flash wins | ||
| IFEval | 69.6% | 79% |
| Multilingual | ||
| MGSM | — | 74% |
| MMLU-ProX | — | 69% |
| Mathematics1-bit Bonsai 4B wins | ||
| MATH-500 | 65.8% | 72% |
| AIME 2023 | — | 50% |
| AIME 2024 | — | 52% |
| AIME 2025 | — | 51% |
| HMMT Feb 2023 | — | 46% |
| HMMT Feb 2024 | — | 48% |
| HMMT Feb 2025 | — | 47% |
| BRUMO 2025 | — | 49% |
Gemini 2.5 Flash is ahead overall, 49 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 49%.
Gemini 2.5 Flash has the edge for knowledge tasks in this comparison, averaging 40.9 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
1-bit Bonsai 4B has the edge for math in this comparison, averaging 65.8 versus 55.6. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Gemini 2.5 Flash has the edge for reasoning in this comparison, averaging 62.1 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Gemini 2.5 Flash has the edge for instruction following in this comparison, averaging 79 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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