Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Mercury 2 is clearly ahead on the aggregate, 65 to 30. 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 reasoning, where it averages 80.1 against 32.1. The single biggest benchmark swing on the page is HumanEval, 75 to 14.
Mercury 2 is also the more expensive model on tokens at $0.25 input / $0.75 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Instruct. That is roughly Infinityx on output cost alone. Mercury 2 is the reasoning model in the pair, while LFM2.5-1.2B-Instruct 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. Mercury 2 gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick Mercury 2 if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Mercury 2
63.7
LFM2.5-1.2B-Instruct
25.7
Mercury 2
41.1
LFM2.5-1.2B-Instruct
7.2
Mercury 2
68.3
LFM2.5-1.2B-Instruct
32.4
Mercury 2
80.1
LFM2.5-1.2B-Instruct
32.1
Mercury 2
57.2
LFM2.5-1.2B-Instruct
26
Mercury 2
84
LFM2.5-1.2B-Instruct
80
Mercury 2
79.7
LFM2.5-1.2B-Instruct
60.7
Mercury 2
80.9
LFM2.5-1.2B-Instruct
37
Mercury 2 is ahead overall, 65 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 75 and 14.
Mercury 2 has the edge for knowledge tasks in this comparison, averaging 57.2 versus 26. 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 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for math in this comparison, averaging 80.9 versus 37. 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 32.1. 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 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Mercury 2 has the edge for multimodal and grounded tasks in this comparison, averaging 68.3 versus 32.4. 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 80. 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 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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