Gemini 3.1 Pro vs Sarvam 105B

Pick Gemini 3.1 Pro 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 3.1 Pro is clearly ahead on the aggregate, 87 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Gemini 3.1 Pro's sharpest advantage is in agentic, where it averages 76.1 against 49.5. The single biggest benchmark swing on the page is BrowseComp, 86% 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 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 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 3.1 Pro 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 3.1 Pro gives you the larger context window at 1M, compared with 128K for Sarvam 105B.