Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3 Pro Deep Think
80
Winner · 4/8 categoriesSarvam 105B
60
1/8 categoriesGemini 3 Pro Deep Think· Sarvam 105B
Pick Gemini 3 Pro Deep Think if you want the stronger benchmark profile. Sarvam 105B only becomes the better choice if knowledge is the priority.
Gemini 3 Pro Deep Think is clearly ahead on the aggregate, 80 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3 Pro Deep Think's sharpest advantage is in agentic, where it averages 78.1 against 49.5. The single biggest benchmark swing on the page is BrowseComp, 87% to 49.5%. Sarvam 105B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Gemini 3 Pro Deep Think gives you the larger context window at 2M, compared with 128K for Sarvam 105B.
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 | Gemini 3 Pro Deep Think | Sarvam 105B |
|---|---|---|
| AgenticGemini 3 Pro Deep Think wins | ||
| Terminal-Bench 2.0 | 77% | — |
| BrowseComp | 87% | 49.5% |
| OSWorld-Verified | 73% | — |
| CodingGemini 3 Pro Deep Think wins | ||
| HumanEval | 91% | — |
| SWE-bench Verified | 58% | 45% |
| LiveCodeBench | 58% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 95% | — |
| OfficeQA Pro | 95% | — |
| Reasoning | ||
| MuSR | 93% | — |
| BBH | 95% | — |
| LongBench v2 | 94% | — |
| MRCRv2 | 96% | — |
| ARC-AGI-2 | 45.1% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 99% | 90.6% |
| GPQA | 97% | — |
| SuperGPQA | 95% | — |
| MMLU-Pro | 81% | 81.7% |
| HLE | 32% | — |
| FrontierScience | 88% | — |
| SimpleQA | 95% | — |
| Instruction FollowingGemini 3 Pro Deep Think wins | ||
| IFEval | 89% | 84.8% |
| Multilingual | ||
| MGSM | 92% | — |
| MMLU-ProX | 85% | — |
| MathematicsGemini 3 Pro Deep Think wins | ||
| AIME 2023 | 99% | — |
| AIME 2024 | 99% | — |
| HMMT Feb 2024 | 97% | — |
| HMMT Feb 2025 | 96% | — |
| BRUMO 2025 | 96% | — |
| MATH-500 | 92% | 98.6% |
| AIME 2025 | — | 88.3% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Gemini 3 Pro Deep Think is ahead overall, 80 to 60. The biggest single separator in this matchup is BrowseComp, where the scores are 87% and 49.5%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 76.4. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for coding in this comparison, averaging 58 versus 45. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for math in this comparison, averaging 94.3 versus 92.3. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for agentic tasks in this comparison, averaging 78.1 versus 49.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Gemini 3 Pro Deep Think has the edge for instruction following in this comparison, averaging 89 versus 84.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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