Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
65
GPT-5.4 mini
70
Pick GPT-5.4 mini if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+3.9 difference
Multimodal
+0.3 difference
Gemma 4 31B
GPT-5.4 mini
$0 / $0
$0.75 / $4.5
N/A
201 t/s
N/A
3.85s
256K
400K
Pick GPT-5.4 mini if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-5.4 mini is clearly ahead on the provisional aggregate, 70 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 mini is also the more expensive model on tokens at $0.75 input / $4.50 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. GPT-5.4 mini gives you the larger context window at 400K, compared with 256K for Gemma 4 31B.
GPT-5.4 mini is ahead on BenchLM's provisional leaderboard, 70 to 65. The biggest single separator in this matchup is HLE, where the scores are 26.5% and 41.5%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 57.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 76.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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