Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
62
GPT-5.4 nano
62
Treat this as a split decision. Gemma 4 31B makes more sense if multimodal & grounded is the priority or you want the cheaper token bill; GPT-5.4 nano is the better fit 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
Treat this as a split decision. Gemma 4 31B makes more sense if multimodal & grounded is the priority or you want the cheaper token bill; GPT-5.4 nano is the better fit if you need the larger 400K context window.
Gemma 4 31B and GPT-5.4 nano finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
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 and GPT-5.4 nano are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 53.2. Inside this category, AA-Omniscience Index 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.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.