Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesDeepSeek Coder 2.0
62
Winner · 4/8 categories1-bit Bonsai 4B· DeepSeek Coder 2.0
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you want the cheaper token bill.
DeepSeek Coder 2.0 is clearly ahead on the aggregate, 62 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek Coder 2.0's sharpest advantage is in knowledge, where it averages 61.1 against 28.7. The single biggest benchmark swing on the page is GPQA, 28.7% to 79%.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 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. DeepSeek Coder 2.0 gives you the larger context window at 128K, 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 | DeepSeek Coder 2.0 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 73% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 65% |
| Coding | ||
| HumanEval | — | 82% |
| SWE-bench Verified | — | 51% |
| LiveCodeBench | — | 45% |
| SWE-bench Pro | — | 61% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 50% |
| OfficeQA Pro | — | 69% |
| ReasoningDeepSeek Coder 2.0 wins | ||
| MuSR | 41.4% | 76% |
| BBH | — | 84% |
| LongBench v2 | — | 73% |
| MRCRv2 | — | 71% |
| KnowledgeDeepSeek Coder 2.0 wins | ||
| GPQA | 28.7% | 79% |
| MMLU | — | 80% |
| SuperGPQA | — | 77% |
| MMLU-Pro | — | 73% |
| HLE | — | 14% |
| FrontierScience | — | 72% |
| SimpleQA | — | 78% |
| Instruction FollowingDeepSeek Coder 2.0 wins | ||
| IFEval | 69.6% | 86% |
| Multilingual | ||
| MGSM | — | 83% |
| MMLU-ProX | — | 78% |
| MathematicsDeepSeek Coder 2.0 wins | ||
| MATH-500 | 65.8% | 81% |
| AIME 2023 | — | 81% |
| AIME 2024 | — | 83% |
| AIME 2025 | — | 82% |
| HMMT Feb 2023 | — | 77% |
| HMMT Feb 2024 | — | 79% |
| HMMT Feb 2025 | — | 78% |
| BRUMO 2025 | — | 80% |
DeepSeek Coder 2.0 is ahead overall, 62 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 79%.
DeepSeek Coder 2.0 has the edge for knowledge tasks in this comparison, averaging 61.1 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for math in this comparison, averaging 81.1 versus 65.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for reasoning in this comparison, averaging 73.1 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for instruction following in this comparison, averaging 86 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.