Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemini 3.1 Pro is clearly ahead on the aggregate, 84 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.1 Pro's sharpest advantage is in coding, where it averages 71.9 against 8.2. The single biggest benchmark swing on the page is HumanEval, 91 to 17.
Gemini 3.1 Pro is also the more expensive model on tokens at $1.25 input / $5.00 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 Pro 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 Pro gives you the larger context window at 1M, compared with 32K for LFM2.5-1.2B-Thinking.
Pick Gemini 3.1 Pro 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 Pro
76.1
LFM2.5-1.2B-Thinking
34.1
Gemini 3.1 Pro
71.9
LFM2.5-1.2B-Thinking
8.2
Gemini 3.1 Pro
95
LFM2.5-1.2B-Thinking
32.4
Gemini 3.1 Pro
92.7
LFM2.5-1.2B-Thinking
38.4
Gemini 3.1 Pro
79.4
LFM2.5-1.2B-Thinking
27
Gemini 3.1 Pro
95
LFM2.5-1.2B-Thinking
72
Gemini 3.1 Pro
94.1
LFM2.5-1.2B-Thinking
60.7
Gemini 3.1 Pro
96.8
LFM2.5-1.2B-Thinking
42.3
Gemini 3.1 Pro is ahead overall, 84 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 91 and 17.
Gemini 3.1 Pro has the edge for knowledge tasks in this comparison, averaging 79.4 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for coding in this comparison, averaging 71.9 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for math in this comparison, averaging 96.8 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for reasoning in this comparison, averaging 92.7 versus 38.4. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for agentic tasks in this comparison, averaging 76.1 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for instruction following in this comparison, averaging 95 versus 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multilingual tasks in this comparison, averaging 94.1 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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