Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
o3
64
Winner · 3/8 categoriesSarvam 105B
60
2/8 categorieso3· Sarvam 105B
Pick o3 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.
o3 is clearly ahead on the aggregate, 64 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3's sharpest advantage is in agentic, where it averages 69.9 against 49.5. The single biggest benchmark swing on the page is SWE-bench Verified, 71.7% to 45%. Sarvam 105B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o3 is also the more expensive model on tokens at $10.00 input / $40.00 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. o3 gives you the larger context window at 200K, 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 | o3 | Sarvam 105B |
|---|---|---|
| Agentico3 wins | ||
| Terminal-Bench 2.0 | 71% | — |
| BrowseComp | 75% | 49.5% |
| OSWorld-Verified | 65% | — |
| Codingo3 wins | ||
| HumanEval | 78% | — |
| SWE-bench Verified | 71.7% | 45% |
| LiveCodeBench | 40% | — |
| SWE-bench Pro | 58% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | 70% | — |
| OfficeQA Pro | 75% | — |
| VideoMMMU | 83.3% | — |
| Reasoning | ||
| MuSR | 82% | — |
| BBH | 86% | — |
| LongBench v2 | 82% | — |
| MRCRv2 | 81% | — |
| ARC-AGI-2 | 3% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 86% | 90.6% |
| MMLU-Pro | 75% | 81.7% |
| HLE | 24% | — |
| FrontierScience | 77% | — |
| Instruction Followingo3 wins | ||
| IFEval | 85% | 84.8% |
| Multilingual | ||
| MGSM | 83% | — |
| MMLU-ProX | 80% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 88% | — |
| AIME 2024 | 90% | — |
| AIME 2025 | 89% | 88.3% |
| HMMT Feb 2023 | 84% | — |
| HMMT Feb 2024 | 86% | — |
| HMMT Feb 2025 | 85% | — |
| BRUMO 2025 | 87% | — |
| MATH-500 | 88% | 98.6% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
o3 is ahead overall, 64 to 60. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 71.7% and 45%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 57. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
o3 has the edge for coding in this comparison, averaging 54.2 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 88.1. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
o3 has the edge for agentic tasks in this comparison, averaging 69.9 versus 49.5. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
o3 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.
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.