Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Phi-4
40
2/8 categoriesSarvam 105B
60
Winner · 2/8 categoriesPhi-4· Sarvam 105B
Pick Sarvam 105B if you want the stronger benchmark profile. Phi-4 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Sarvam 105B is clearly ahead on the aggregate, 60 to 40. 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 53.6. The single biggest benchmark swing on the page is BrowseComp, 35% to 49.5%. Phi-4 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Sarvam 105B is the reasoning model in the pair, while Phi-4 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 16K for Phi-4.
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 | Phi-4 | Sarvam 105B |
|---|---|---|
| AgenticSarvam 105B wins | ||
| Terminal-Bench 2.0 | 44% | — |
| BrowseComp | 35% | 49.5% |
| OSWorld-Verified | 34% | — |
| CodingPhi-4 wins | ||
| HumanEval | 82.6% | — |
| SWE-bench Pro | 55% | — |
| LiveCodeBench v6 | — | 71.7% |
| SWE-bench Verified | — | 45% |
| Multimodal & Grounded | ||
| MMMU-Pro | 54% | — |
| OfficeQA Pro | 38% | — |
| Reasoning | ||
| LongBench v2 | 30% | — |
| MRCRv2 | 33% | — |
| gpqaDiamond | — | 78.7% |
| hle | — | 11.2% |
| KnowledgeSarvam 105B wins | ||
| MMLU | 84.8% | 90.6% |
| GPQA | 56.1% | — |
| FrontierScience | 52% | — |
| MMLU-Pro | — | 81.7% |
| Instruction Following | ||
| IFEval | — | 84.8% |
| Multilingual | ||
| MGSM | 80.6% | — |
| MMLU-ProX | 60% | — |
| MathematicsPhi-4 wins | ||
| MATH-500 | 94.6% | 98.6% |
| AIME 2025 | — | 88.3% |
| HMMT Feb 2025 | — | 85.8% |
| HMMT Nov 2025 | — | 85.8% |
Sarvam 105B is ahead overall, 60 to 40. The biggest single separator in this matchup is BrowseComp, where the scores are 35% and 49.5%.
Sarvam 105B has the edge for knowledge tasks in this comparison, averaging 81.7 versus 53.6. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 55 versus 45. Sarvam 105B stays close enough that the answer can still flip depending on your workload.
Phi-4 has the edge for math in this comparison, averaging 94.6 versus 92.3. 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 38.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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