Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0
62
Winner · 3/8 categoriesSarvam 105B
60
2/8 categoriesDeepSeek Coder 2.0· Sarvam 105B
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. Sarvam 105B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
DeepSeek Coder 2.0 has the cleaner overall profile here, landing at 62 versus 60. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek Coder 2.0's sharpest advantage is in agentic, where it averages 67.5 against 49.5. The single biggest benchmark swing on the page is MATH-500, 81% to 98.6%. Sarvam 105B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
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 Sarvam 105B. That is roughly Infinityx on output cost alone. Sarvam 105B is the reasoning model in the pair, while DeepSeek Coder 2.0 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.
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 | Sarvam 105B |
|---|---|---|
| AgenticDeepSeek Coder 2.0 wins | ||
| Terminal-Bench 2.0 | 73% | — |
| BrowseComp | 62% | 49.5% |
| OSWorld-Verified | 65% | — |
| CodingDeepSeek Coder 2.0 wins | ||
| HumanEval | 82% | — |
| SWE-bench Verified | 51% | 45% |
| LiveCodeBench | 45% | — |
| SWE-bench Pro | 61% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 50% | — |
| OfficeQA Pro | 69% | — |
| Reasoning | ||
| MuSR | 76% | — |
| BBH | 84% | — |
| LongBench v2 | 73% | — |
| MRCRv2 | 71% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 80% | 90.6% |
| GPQA | 79% | — |
| SuperGPQA | 77% | — |
| MMLU-Pro | 73% | 81.7% |
| HLE | 14% | — |
| FrontierScience | 72% | — |
| SimpleQA | 78% | — |
| Instruction FollowingDeepSeek Coder 2.0 wins | ||
| IFEval | 86% | 84.8% |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 78% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 81% | — |
| AIME 2024 | 83% | — |
| AIME 2025 | 82% | 88.3% |
| HMMT Feb 2023 | 77% | — |
| HMMT Feb 2024 | 79% | — |
| HMMT Feb 2025 | 78% | — |
| BRUMO 2025 | 80% | — |
| MATH-500 | 81% | 98.6% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
DeepSeek Coder 2.0 is ahead overall, 62 to 60. The biggest single separator in this matchup is MATH-500, where the scores are 81% and 98.6%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 61.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for coding in this comparison, averaging 52.5 versus 45. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for math in this comparison, averaging 92.3 versus 81.1. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for agentic tasks in this comparison, averaging 67.5 versus 49.5. Inside this category, BrowseComp 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 84.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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