Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 3.1 Flash-Lite
47
GPT-5.4 nano
59
Pick GPT-5.4 nano if you want the stronger benchmark profile. Gemini 3.1 Flash-Lite only becomes the better choice if multimodal & grounded is the priority or you need the larger 1M context window.
Multimodal
+7.1 difference
Gemini 3.1 Flash-Lite
GPT-5.4 nano
$0.25 / $1.5
$0.2 / $1.25
205 t/s
191 t/s
7.50s
3.64s
1M
400K
Pick GPT-5.4 nano if you want the stronger benchmark profile. Gemini 3.1 Flash-Lite only becomes the better choice if multimodal & grounded is the priority or you need the larger 1M context window.
GPT-5.4 nano is clearly ahead on the provisional aggregate, 59 to 47. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Flash-Lite is also the more expensive model on tokens at $0.25 input / $1.50 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. GPT-5.4 nano is the reasoning model in the pair, while Gemini 3.1 Flash-Lite 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. Gemini 3.1 Flash-Lite gives you the larger context window at 1M, compared with 400K for GPT-5.4 nano.
GPT-5.4 nano is ahead on BenchLM's provisional leaderboard, 59 to 47.
Gemini 3.1 Flash-Lite has the edge for multimodal and grounded tasks in this comparison, averaging 73.2 versus 66.1. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.