Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3.1 Flash-Lite is clearly ahead on the aggregate, 53 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Flash-Lite's sharpest advantage is in multimodal & grounded, where it averages 73.1 against 32.4. The single biggest benchmark swing on the page is MMMU-Pro, 74 to 27.
Gemini 3.1 Flash-Lite is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Thinking. That is roughly Infinityx on output cost alone. LFM2.5-1.2B-Thinking 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 32K for LFM2.5-1.2B-Thinking.
Pick Gemini 3.1 Flash-Lite if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Gemini 3.1 Flash-Lite
49.2
LFM2.5-1.2B-Thinking
34.1
Gemini 3.1 Flash-Lite
24.7
LFM2.5-1.2B-Thinking
8.2
Gemini 3.1 Flash-Lite
73.1
LFM2.5-1.2B-Thinking
32.4
Gemini 3.1 Flash-Lite
65.8
LFM2.5-1.2B-Thinking
38.4
Gemini 3.1 Flash-Lite
45.3
LFM2.5-1.2B-Thinking
27
Gemini 3.1 Flash-Lite
79
LFM2.5-1.2B-Thinking
72
Gemini 3.1 Flash-Lite
69.8
LFM2.5-1.2B-Thinking
60.7
Gemini 3.1 Flash-Lite
66.1
LFM2.5-1.2B-Thinking
42.3
Gemini 3.1 Flash-Lite is ahead overall, 53 to 33. The biggest single separator in this matchup is MMMU-Pro, where the scores are 74 and 27.
Gemini 3.1 Flash-Lite has the edge for knowledge tasks in this comparison, averaging 45.3 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for coding in this comparison, averaging 24.7 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for math in this comparison, averaging 66.1 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for reasoning in this comparison, averaging 65.8 versus 38.4. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for agentic tasks in this comparison, averaging 49.2 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for multimodal and grounded tasks in this comparison, averaging 73.1 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for instruction following in this comparison, averaging 79 versus 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Flash-Lite has the edge for multilingual tasks in this comparison, averaging 69.8 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.