Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 53. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mercury 2's sharpest advantage is in coding, where it averages 41.1 against 24.7. The single biggest benchmark swing on the page is SWE-bench Verified, 46 to 22. Gemini 3.1 Flash-Lite does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Mercury 2 is also the more expensive model on tokens at $0.25 input / $0.75 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for Gemini 3.1 Flash-Lite. Mercury 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 128K for Mercury 2.
Pick Mercury 2 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 want the cheaper token bill.
Mercury 2
63.7
Gemini 3.1 Flash-Lite
49.2
Mercury 2
41.1
Gemini 3.1 Flash-Lite
24.7
Mercury 2
68.3
Gemini 3.1 Flash-Lite
73.1
Mercury 2
80.1
Gemini 3.1 Flash-Lite
65.8
Mercury 2
57.2
Gemini 3.1 Flash-Lite
45.3
Mercury 2
84
Gemini 3.1 Flash-Lite
79
Mercury 2
79.7
Gemini 3.1 Flash-Lite
69.8
Mercury 2
80.9
Gemini 3.1 Flash-Lite
66.1
Mercury 2 is ahead overall, 65 to 53. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 46 and 22.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 45.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for coding in this comparison, averaging 41.1 versus 24.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for math in this comparison, averaging 80.9 versus 66.1. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for reasoning in this comparison, averaging 80.1 versus 65.8. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for agentic tasks in this comparison, averaging 63.7 versus 49.2. Inside this category, OSWorld-Verified 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 68.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for instruction following in this comparison, averaging 84 versus 79. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for multilingual tasks in this comparison, averaging 79.7 versus 69.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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