Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
64
MAI-Thinking-1
65
Verified leaderboard positions: Gemma 4 31B unranked · MAI-Thinking-1 #23
Pick MAI-Thinking-1 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Coding
+29.4 difference
Knowledge
+8.6 difference
Gemma 4 31B
MAI-Thinking-1
$0 / $0
N/A
N/A
N/A
N/A
N/A
256K
256K
Pick MAI-Thinking-1 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
MAI-Thinking-1 finishes one point ahead on BenchLM's provisional leaderboard, 65 to 64. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
MAI-Thinking-1's sharpest advantage is in coding, where it averages 71 against 41.6. The single biggest benchmark swing on the page is MMLU-Pro, 85.2% to 85%.
MAI-Thinking-1 is ahead on BenchLM's provisional leaderboard, 65 to 64. The biggest single separator in this matchup is MMLU-Pro, where the scores are 85.2% and 85%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 61.3. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
MAI-Thinking-1 has the edge for coding in this comparison, averaging 71 versus 41.6. Gemma 4 31B 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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