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 64. 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 44. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 40.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Claude 4.1 Opus 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 200K for Claude 4.1 Opus.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Claude 4.1 Opus only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3-Codex-Spark
85.6
Claude 4.1 Opus
58.7
GPT-5.3-Codex-Spark
82.3
Claude 4.1 Opus
44
GPT-5.3-Codex-Spark
88.3
Claude 4.1 Opus
80.7
GPT-5.3-Codex-Spark
92.7
Claude 4.1 Opus
72.9
GPT-5.3-Codex-Spark
78.3
Claude 4.1 Opus
57.6
GPT-5.3-Codex-Spark
92
Claude 4.1 Opus
83
GPT-5.3-Codex-Spark
90.8
Claude 4.1 Opus
81.8
GPT-5.3-Codex-Spark
96.7
Claude 4.1 Opus
77.7
GPT-5.3-Codex-Spark is ahead overall, 87 to 64. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 40.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 57.6. Inside this category, HLE 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 44. Inside this category, LiveCodeBench 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 77.7. 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 72.9. Inside this category, MRCRv2 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 58.7. 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 80.7. Inside this category, OfficeQA 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 83. 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 81.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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