Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3 Flash
67
Winner · 2/8 categoriesSarvam 105B
60
2/8 categoriesGemini 3 Flash· Sarvam 105B
Pick Gemini 3 Flash 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.
Gemini 3 Flash is clearly ahead on the aggregate, 67 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3 Flash's sharpest advantage is in agentic, where it averages 57.5 against 49.5. The single biggest benchmark swing on the page is MMLU, 70% to 90.6%. 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 Flash is also the more expensive model on tokens at $0.50 input / $3.00 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 Gemini 3 Flash 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. Gemini 3 Flash gives you the larger context window at 1M, 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 Flash | Sarvam 105B |
|---|---|---|
| AgenticGemini 3 Flash wins | ||
| Terminal-Bench 2.0 | 56% | — |
| BrowseComp | 66% | 49.5% |
| OSWorld-Verified | 53% | — |
| Claw-Eval | 47.1% | — |
| CodingTie | ||
| HumanEval | 62% | — |
| SWE-bench Verified | 44% | 45% |
| LiveCodeBench | 36% | — |
| SWE-bench Pro | 44% | — |
| SWE-Rebench | 52.5% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 80% | — |
| OfficeQA Pro | 79% | — |
| Reasoning | ||
| MuSR | 65% | — |
| BBH | 84% | — |
| LongBench v2 | 75% | — |
| MRCRv2 | 76% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 70% | 90.6% |
| GPQA | 69% | — |
| SuperGPQA | 67% | — |
| MMLU-Pro | 72% | 81.7% |
| HLE | 6% | — |
| FrontierScience | 65% | — |
| SimpleQA | 67% | — |
| Instruction FollowingGemini 3 Flash wins | ||
| IFEval | 85% | 84.8% |
| Multilingual | ||
| MGSM | 85% | — |
| MMLU-ProX | 78% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 70% | — |
| AIME 2024 | 72% | — |
| AIME 2025 | 71% | 88.3% |
| HMMT Feb 2023 | 66% | — |
| HMMT Feb 2024 | 68% | — |
| HMMT Feb 2025 | 67% | — |
| BRUMO 2025 | 69% | — |
| MATH-500 | 80% | 98.6% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Gemini 3 Flash is ahead overall, 67 to 60. The biggest single separator in this matchup is MMLU, where the scores are 70% and 90.6%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 54. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Gemini 3 Flash and Sarvam 105B are effectively tied for coding here, both landing at 45 on average.
Sarvam 105B has the edge for math in this comparison, averaging 92.3 versus 72.6. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Gemini 3 Flash has the edge for agentic tasks in this comparison, averaging 57.5 versus 49.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Gemini 3 Flash has the edge for instruction following in this comparison, averaging 85 versus 84.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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