Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Qwen3 235B 2507 (Reasoning) is clearly ahead on the aggregate, 42 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3 235B 2507 (Reasoning)'s sharpest advantage is in agentic, where it averages 45.9 against 33.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 47 to 30. LFM2-24B-A2B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while LFM2-24B-A2B 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. Qwen3 235B 2507 (Reasoning) gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick Qwen3 235B 2507 (Reasoning) if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if multilingual is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3 235B 2507 (Reasoning)
45.9
LFM2-24B-A2B
33.4
Qwen3 235B 2507 (Reasoning)
22.8
LFM2-24B-A2B
18
Qwen3 235B 2507 (Reasoning)
42.1
LFM2-24B-A2B
41.7
Qwen3 235B 2507 (Reasoning)
49
LFM2-24B-A2B
46.6
Qwen3 235B 2507 (Reasoning)
34.1
LFM2-24B-A2B
35.6
Qwen3 235B 2507 (Reasoning)
68
LFM2-24B-A2B
68
Qwen3 235B 2507 (Reasoning)
58.8
LFM2-24B-A2B
61.4
Qwen3 235B 2507 (Reasoning)
48.5
LFM2-24B-A2B
50.4
Qwen3 235B 2507 (Reasoning) is ahead overall, 42 to 38. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 47 and 30.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 34.1. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for coding in this comparison, averaging 22.8 versus 18. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for math in this comparison, averaging 50.4 versus 48.5. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for reasoning in this comparison, averaging 49 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for agentic tasks in this comparison, averaging 45.9 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 42.1 versus 41.7. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) and LFM2-24B-A2B are effectively tied for instruction following here, both landing at 68 on average.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 58.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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