Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark is clearly ahead on the aggregate, 87 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-Spark's sharpest advantage is in coding, where it averages 82.3 against 7.2. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 6.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 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-Spark 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-Spark gives you the larger context window at 256K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GPT-5.3-Codex-Spark 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-Spark
85.6
LFM2.5-1.2B-Instruct
25.7
GPT-5.3-Codex-Spark
82.3
LFM2.5-1.2B-Instruct
7.2
GPT-5.3-Codex-Spark
88.3
LFM2.5-1.2B-Instruct
32.4
GPT-5.3-Codex-Spark
92.7
LFM2.5-1.2B-Instruct
32.1
GPT-5.3-Codex-Spark
78.3
LFM2.5-1.2B-Instruct
26
GPT-5.3-Codex-Spark
92
LFM2.5-1.2B-Instruct
80
GPT-5.3-Codex-Spark
90.8
LFM2.5-1.2B-Instruct
60.7
GPT-5.3-Codex-Spark
96.7
LFM2.5-1.2B-Instruct
37
GPT-5.3-Codex-Spark is ahead overall, 87 to 30. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 6.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for coding in this comparison, averaging 82.3 versus 7.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for math in this comparison, averaging 96.7 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for reasoning in this comparison, averaging 92.7 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for agentic tasks in this comparison, averaging 85.6 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multimodal and grounded tasks in this comparison, averaging 88.3 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for instruction following in this comparison, averaging 92 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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