Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5 nano is clearly ahead on the aggregate, 45 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5 nano's sharpest advantage is in mathematics, where it averages 85.2 against 50.4. The single biggest benchmark swing on the page is AIME 2025, 85.2 to 47. LFM2-24B-A2B does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
GPT-5 nano is also the more expensive model on tokens at $0.05 input / $0.40 output per 1M tokens, versus $0.03 input / $0.12 output per 1M tokens for LFM2-24B-A2B. That is roughly 3.3x on output cost alone. GPT-5 nano 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. GPT-5 nano gives you the larger context window at 400K, compared with 32K for LFM2-24B-A2B.
Pick GPT-5 nano if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if multilingual is the priority or you want the cheaper token bill.
GPT-5 nano
37.7
LFM2-24B-A2B
33.4
GPT-5 nano
22
LFM2-24B-A2B
18
GPT-5 nano
56.7
LFM2-24B-A2B
41.7
GPT-5 nano
58.8
LFM2-24B-A2B
46.6
GPT-5 nano
63.7
LFM2-24B-A2B
35.6
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-5 nano
48
LFM2-24B-A2B
61.4
GPT-5 nano
85.2
LFM2-24B-A2B
50.4
GPT-5 nano is ahead overall, 45 to 38. The biggest single separator in this matchup is AIME 2025, where the scores are 85.2 and 47.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 63.7 versus 35.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for coding in this comparison, averaging 22 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for math in this comparison, averaging 85.2 versus 50.4. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for reasoning in this comparison, averaging 58.8 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for agentic tasks in this comparison, averaging 37.7 versus 33.4. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for multimodal and grounded tasks in this comparison, averaging 56.7 versus 41.7. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 48. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.