Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 26B A4B
55
MiniMax M3
76
Verified leaderboard positions: Gemma 4 26B A4B unranked · MiniMax M3 #12
Pick MiniMax M3 if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
Multimodal
+8.9 difference
Gemma 4 26B A4B
MiniMax M3
$0 / $0
$0.3 / $1.2
N/A
N/A
N/A
N/A
256K
1M
Pick MiniMax M3 if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
MiniMax M3 is clearly ahead on the provisional aggregate, 76 to 55. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M3 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 26B A4B. That is roughly Infinityx on output cost alone. Gemma 4 26B A4B is the reasoning model in the pair, while MiniMax M3 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. MiniMax M3 gives you the larger context window at 1M, compared with 256K for Gemma 4 26B A4B.
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 76 to 55. The biggest single separator in this matchup is MMMU-Pro, where the scores are 73.8% and 78.1%.
Gemma 4 26B A4B has the edge for multimodal and grounded tasks in this comparison, averaging 73.8 versus 64.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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