Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
64
Mellum2-12B-A2.5B-Instruct
27
Pick Gemma 4 31B if you want the stronger benchmark profile. Mellum2-12B-A2.5B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+4.4 difference
Knowledge
+20.4 difference
Gemma 4 31B
Mellum2-12B-A2.5B-Instruct
$0 / $0
N/A
N/A
N/A
N/A
N/A
256K
128K
Pick Gemma 4 31B if you want the stronger benchmark profile. Mellum2-12B-A2.5B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 31B is clearly ahead on the provisional aggregate, 64 to 27. 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 40.9. The single biggest benchmark swing on the page is GPQA, 84.3% to 40.9%.
Gemma 4 31B is the reasoning model in the pair, while Mellum2-12B-A2.5B-Instruct 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. Gemma 4 31B gives you the larger context window at 256K, compared with 128K for Mellum2-12B-A2.5B-Instruct.
Gemma 4 31B is ahead on BenchLM's provisional leaderboard, 64 to 27. The biggest single separator in this matchup is GPQA, where the scores are 84.3% and 40.9%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 40.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for coding in this comparison, averaging 41.6 versus 37.2. Mellum2-12B-A2.5B-Instruct stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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