Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3 Codex is clearly ahead on the aggregate, 89 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in coding, where it averages 87.3 against 7.2. The single biggest benchmark swing on the page is SWE-bench Pro, 90 to 6.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 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. GPT-5.3 Codex 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. GPT-5.3 Codex gives you the larger context window at 400K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GPT-5.3 Codex 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.
GPT-5.3 Codex
88.1
LFM2.5-1.2B-Instruct
25.7
GPT-5.3 Codex
87.3
LFM2.5-1.2B-Instruct
7.2
GPT-5.3 Codex
91.3
LFM2.5-1.2B-Instruct
32.4
GPT-5.3 Codex
93.7
LFM2.5-1.2B-Instruct
32.1
GPT-5.3 Codex
80.3
LFM2.5-1.2B-Instruct
26
GPT-5.3 Codex
93
LFM2.5-1.2B-Instruct
80
GPT-5.3 Codex
92.8
LFM2.5-1.2B-Instruct
60.7
GPT-5.3 Codex
97.7
LFM2.5-1.2B-Instruct
37
GPT-5.3 Codex is ahead overall, 89 to 30. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 90 and 6.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 80.3 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 87.3 versus 7.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for math in this comparison, averaging 97.7 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for reasoning in this comparison, averaging 93.7 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 88.1 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multimodal and grounded tasks in this comparison, averaging 91.3 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for instruction following in this comparison, averaging 93 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multilingual tasks in this comparison, averaging 92.8 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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