Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 2.5 Pro
67
Gemma 4 31B
74
Pick Gemma 4 31B if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+20.5 difference
Gemini 2.5 Pro
Gemma 4 31B
$1.25 / $5
$0 / $0
117 t/s
N/A
21.19s
N/A
1M
256K
Pick Gemma 4 31B if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B is clearly ahead on the provisional aggregate, 74 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 31B's sharpest advantage is in knowledge, where it averages 61.3 against 40.8. The single biggest benchmark swing on the page is HLE, 18.8% to 26.5%.
Gemini 2.5 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 Gemma 4 31B. That is roughly Infinityx on output cost alone. Gemma 4 31B is the reasoning model in the pair, while Gemini 2.5 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 2.5 Pro gives you the larger context window at 1M, compared with 256K for Gemma 4 31B.
Gemma 4 31B is ahead on BenchLM's provisional leaderboard, 74 to 67. The biggest single separator in this matchup is HLE, where the scores are 18.8% and 26.5%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 40.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
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