Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Llama 4 Behemoth
34
0/8 categoriesSarvam 105B
60
Winner · 5/8 categoriesLlama 4 Behemoth· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. Llama 4 Behemoth only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 105B is clearly ahead on the aggregate, 60 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Sarvam 105B's sharpest advantage is in knowledge, where it averages 81.7 against 37.3. The single biggest benchmark swing on the page is MMLU, 48% to 90.6%.
Sarvam 105B is the reasoning model in the pair, while Llama 4 Behemoth 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. Sarvam 105B gives you the larger context window at 128K, compared with 32K for Llama 4 Behemoth.
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 | Llama 4 Behemoth | Sarvam 105B |
|---|---|---|
| AgenticSarvam 105B wins | ||
| Terminal-Bench 2.0 | 33% | — |
| BrowseComp | — | 49.5% |
| CodingSarvam 105B wins | ||
| HumanEval | 40% | — |
| SWE-bench Verified | 15% | 45% |
| LiveCodeBench | 13% | — |
| SWE-bench Pro | 15% | — |
| LiveCodeBench v6 | — | 71.7% |
| Multimodal & Grounded | ||
| OfficeQA Pro | 49% | — |
| Reasoning | ||
| MuSR | 44% | — |
| BBH | 62% | — |
| LongBench v2 | 46% | — |
| MRCRv2 | 46% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 48% | 90.6% |
| GPQA | 47% | — |
| SuperGPQA | 45% | — |
| MMLU-Pro | 54% | 81.7% |
| HLE | 3% | — |
| FrontierScience | 43% | — |
| SimpleQA | 46% | — |
| Instruction FollowingSarvam 105B wins | ||
| IFEval | 68% | 84.8% |
| Multilingual | ||
| MMLU-ProX | 61% | — |
| MathematicsSarvam 105B wins | ||
| AIME 2023 | 48% | — |
| AIME 2024 | 50% | — |
| HMMT Feb 2024 | 46% | — |
| HMMT Feb 2025 | 45% | — |
| BRUMO 2025 | 47% | — |
| MATH-500 | 60% | 98.6% |
| AIME 2025 | — | 88.3% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Sarvam 105B is ahead overall, 60 to 34. The biggest single separator in this matchup is MMLU, where the scores are 48% and 90.6%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 37.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for coding in this comparison, averaging 45 versus 14.2. 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 52.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Sarvam 105B has the edge for agentic tasks in this comparison, averaging 49.5 versus 33. Llama 4 Behemoth stays close enough that the answer can still flip depending on your workload.
Sarvam 105B has the edge for instruction following in this comparison, averaging 84.8 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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