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Gemini 3.1 Flash-Lite vs GPT-5.4 nano

Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.

Gemini 3.1 Flash-Lite

48

VS

GPT-5.4 nano

60

1 categoriesvs0 categories

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.

Category Radar

Head-to-Head by Category

Category Breakdown

Multimodal

Gemini 3.1 Flash-Lite
73.2vs66.1

+7.1 difference

Operational Comparison

Gemini 3.1 Flash-Lite

GPT-5.4 nano

Price (per 1M tokens)

$0.25 / $1.5

$0.2 / $1.25

Speed

205 t/s

191 t/s

Latency (TTFT)

7.50s

3.64s

Context Window

1M

400K

Quick Verdict

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, 60 to 48. 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.

Benchmark Deep Dive

Frequently Asked Questions (2)

Which is better, Gemini 3.1 Flash-Lite or GPT-5.4 nano?

GPT-5.4 nano is ahead on BenchLM's provisional leaderboard, 60 to 48.

Which is better for multimodal and grounded tasks, Gemini 3.1 Flash-Lite or GPT-5.4 nano?

Gemini 3.1 Flash-Lite has the edge for multimodal and grounded tasks in this comparison, averaging 73.2 versus 66.1. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.

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Last updated: May 1, 2026

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