Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
65
MiniMax M2.7
62
Pick Gemma 4 31B if you want the stronger benchmark profile. MiniMax M2.7 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.
Coding
+12.1 difference
Gemma 4 31B
MiniMax M2.7
$0 / $0
$0.3 / $1.2
N/A
45 t/s
N/A
2.53s
256K
200K
Pick Gemma 4 31B if you want the stronger benchmark profile. MiniMax M2.7 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.
Gemma 4 31B has the cleaner provisional overall profile here, landing at 65 versus 62. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 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 MiniMax M2.7 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 200K for MiniMax M2.7.
Gemma 4 31B is ahead on BenchLM's provisional leaderboard, 65 to 62. The biggest single separator in this matchup is SWE-Rebench, where the scores are 41.6% and 51.9%.
MiniMax M2.7 has the edge for coding in this comparison, averaging 53.7 versus 41.6. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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