Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0
62
Winner · 3/8 categoriesGranite-4.0-1B
~40
0/8 categoriesDeepSeek Coder 2.0· Granite-4.0-1B
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. Granite-4.0-1B only becomes the better choice if you want the cheaper token bill.
DeepSeek Coder 2.0 is clearly ahead on the aggregate, 62 to 40. 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 multilingual, where it averages 79.8 against 27.5. The single biggest benchmark swing on the page is MGSM, 83% to 27.5%.
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 Granite-4.0-1B. That is roughly Infinityx on output cost alone.
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 | DeepSeek Coder 2.0 | Granite-4.0-1B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | 73% | — |
| BrowseComp | 62% | — |
| OSWorld-Verified | 65% | — |
| Coding | ||
| HumanEval | 82% | 73% |
| SWE-bench Verified | 51% | — |
| LiveCodeBench | 45% | — |
| SWE-bench Pro | 61% | — |
| Multimodal & Grounded | ||
| MMMU-Pro | 50% | — |
| OfficeQA Pro | 69% | — |
| Reasoning | ||
| MuSR | 76% | — |
| BBH | 84% | 59.7% |
| LongBench v2 | 73% | — |
| MRCRv2 | 71% | — |
| KnowledgeDeepSeek Coder 2.0 wins | ||
| MMLU | 80% | 59.7% |
| GPQA | 79% | 29.7% |
| SuperGPQA | 77% | — |
| MMLU-Pro | 73% | 32.9% |
| HLE | 14% | — |
| FrontierScience | 72% | — |
| SimpleQA | 78% | — |
| Instruction FollowingDeepSeek Coder 2.0 wins | ||
| IFEval | 86% | 78.5% |
| MultilingualDeepSeek Coder 2.0 wins | ||
| MGSM | 83% | 27.5% |
| MMLU-ProX | 78% | — |
| Mathematics | ||
| AIME 2023 | 81% | — |
| AIME 2024 | 83% | — |
| AIME 2025 | 82% | — |
| HMMT Feb 2023 | 77% | — |
| HMMT Feb 2024 | 79% | — |
| HMMT Feb 2025 | 78% | — |
| BRUMO 2025 | 80% | — |
| MATH-500 | 81% | — |
DeepSeek Coder 2.0 is ahead overall, 62 to 40. The biggest single separator in this matchup is MGSM, where the scores are 83% and 27.5%.
DeepSeek Coder 2.0 has the edge for knowledge tasks in this comparison, averaging 61.1 versus 31.7. Inside this category, GPQA 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 78.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for multilingual tasks in this comparison, averaging 79.8 versus 27.5. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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