Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
74
GPT-5.4 nano
63
Pick Gemma 4 31B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Knowledge
+8.1 difference
Multimodal
+10.8 difference
Gemma 4 31B
GPT-5.4 nano
$0 / $0
$0.2 / $1.25
N/A
191 t/s
N/A
3.64s
256K
400K
Pick Gemma 4 31B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Gemma 4 31B is clearly ahead on the provisional aggregate, 74 to 63. 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 multimodal & grounded, where it averages 76.9 against 66.1. The single biggest benchmark swing on the page is HLE, 26.5% to 37.7%.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 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 nano gives you the larger context window at 400K, compared with 256K for Gemma 4 31B.
Gemma 4 31B is ahead on BenchLM's provisional leaderboard, 74 to 63. The biggest single separator in this matchup is HLE, where the scores are 26.5% and 37.7%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 53.2. 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 66.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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