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 35. 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 14.8. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 15.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while DeepSeek V3.1 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 128K for DeepSeek V3.1.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. DeepSeek V3.1 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
DeepSeek V3.1
32.9
GPT-5.3-Codex-Spark
82.3
DeepSeek V3.1
14.8
GPT-5.3-Codex-Spark
88.3
DeepSeek V3.1
39.5
GPT-5.3-Codex-Spark
92.7
DeepSeek V3.1
40.7
GPT-5.3-Codex-Spark
78.3
DeepSeek V3.1
30.5
GPT-5.3-Codex-Spark
92
DeepSeek V3.1
67
GPT-5.3-Codex-Spark
90.8
DeepSeek V3.1
60.8
GPT-5.3-Codex-Spark
96.7
DeepSeek V3.1
44.2
GPT-5.3-Codex-Spark is ahead overall, 87 to 35. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 15.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 30.5. 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 14.8. 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 44.2. 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 40.7. 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 32.9. 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 39.5. 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 67. 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 60.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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