Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 3.1 Flash-Lite
48
GPT-5.2
79
Pick GPT-5.2 if you want the stronger benchmark profile. Gemini 3.1 Flash-Lite only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Multimodal
+7.1 difference
Gemini 3.1 Flash-Lite
GPT-5.2
$0.25 / $1.5
$1.75 / $14
205 t/s
73 t/s
7.50s
130.34s
1M
400K
Pick GPT-5.2 if you want the stronger benchmark profile. Gemini 3.1 Flash-Lite only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
GPT-5.2 is clearly ahead on the provisional aggregate, 79 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in multimodal & grounded, where it averages 80.3 against 73.2. The single biggest benchmark swing on the page is CharXiv, 73.2% to 82.1%.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.25 input / $1.50 output per 1M tokens for Gemini 3.1 Flash-Lite. That is roughly 9.3x on output cost alone. GPT-5.2 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-5.2.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 79 to 48. The biggest single separator in this matchup is CharXiv, where the scores are 73.2% and 82.1%.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 80.3 versus 73.2. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
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