Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesGemini 2.5 Pro
65
Winner · 3/8 categories1-bit Bonsai 8B· Gemini 2.5 Pro
Pick Gemini 2.5 Pro if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if you want the cheaper token bill.
Gemini 2.5 Pro is clearly ahead on the aggregate, 65 to 50. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 2.5 Pro's sharpest advantage is in knowledge, where it averages 63.9 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 83%.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $5.00 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. Gemini 2.5 Pro gives you the larger context window at 1M, 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 | Gemini 2.5 Pro |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 61% |
| BrowseComp | — | 72% |
| OSWorld-Verified | — | 55% |
| Coding | ||
| HumanEval | — | 75% |
| SWE-bench Verified | — | 63.8% |
| LiveCodeBench | — | 37% |
| SWE-bench Pro | — | 44% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 86% |
| OfficeQA Pro | — | 84% |
| ReasoningGemini 2.5 Pro wins | ||
| MuSR | 50% | 79% |
| LongBench v2 | — | 80% |
| MRCRv2 | — | 83% |
| ARC-AGI-2 | — | 4.9% |
| KnowledgeGemini 2.5 Pro wins | ||
| GPQA | 30% | 83% |
| MMLU | — | 83% |
| SuperGPQA | — | 81% |
| MMLU-Pro | — | 76% |
| HLE | — | 18.8% |
| FrontierScience | — | 70% |
| SimpleQA | — | 81% |
| Instruction Following | ||
| IFEval | 79.8% | — |
| Multilingual | ||
| MGSM | — | 84% |
| MMLU-ProX | — | 82% |
| MathematicsGemini 2.5 Pro wins | ||
| MATH-500 | 66% | 84% |
| AIME 2023 | — | 84% |
| AIME 2024 | — | 92% |
| AIME 2025 | — | 85% |
| HMMT Feb 2023 | — | 80% |
| HMMT Feb 2024 | — | 82% |
| HMMT Feb 2025 | — | 81% |
| BRUMO 2025 | — | 83% |
Gemini 2.5 Pro is ahead overall, 65 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 83%.
Gemini 2.5 Pro has the edge for knowledge tasks in this comparison, averaging 63.9 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for math in this comparison, averaging 84.1 versus 66. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for reasoning in this comparison, averaging 61.8 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
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