Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-4 Turbo is clearly ahead on the aggregate, 47 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-4 Turbo's sharpest advantage is in multimodal & grounded, where it averages 55.3 against 32.4. The single biggest benchmark swing on the page is HumanEval, 52 to 17.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while GPT-4 Turbo 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. GPT-4 Turbo gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GPT-4 Turbo if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if you want the stronger reasoning-first profile.
GPT-4 Turbo
44.7
LFM2.5-1.2B-Thinking
34.1
GPT-4 Turbo
17.2
LFM2.5-1.2B-Thinking
8.2
GPT-4 Turbo
55.3
LFM2.5-1.2B-Thinking
32.4
GPT-4 Turbo
61
LFM2.5-1.2B-Thinking
38.4
GPT-4 Turbo
41.1
LFM2.5-1.2B-Thinking
27
GPT-4 Turbo
80
LFM2.5-1.2B-Thinking
72
GPT-4 Turbo
68.5
LFM2.5-1.2B-Thinking
60.7
GPT-4 Turbo
64.4
LFM2.5-1.2B-Thinking
42.3
GPT-4 Turbo is ahead overall, 47 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 52 and 17.
GPT-4 Turbo has the edge for knowledge tasks in this comparison, averaging 41.1 versus 27. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for coding in this comparison, averaging 17.2 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for math in this comparison, averaging 64.4 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for reasoning in this comparison, averaging 61 versus 38.4. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for agentic tasks in this comparison, averaging 44.7 versus 34.1. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for multimodal and grounded tasks in this comparison, averaging 55.3 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for instruction following in this comparison, averaging 80 versus 72. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-4 Turbo has the edge for multilingual tasks in this comparison, averaging 68.5 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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