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 66. 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 multimodal & grounded, where it averages 88.3 against 58.6. The single biggest benchmark swing on the page is MMMU-Pro, 86 to 50.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek Coder 2.0. That is roughly 7.3x on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while DeepSeek Coder 2.0 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 Coder 2.0.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. DeepSeek Coder 2.0 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
DeepSeek Coder 2.0
67.5
GPT-5.3-Codex-Spark
82.3
DeepSeek Coder 2.0
52.8
GPT-5.3-Codex-Spark
88.3
DeepSeek Coder 2.0
58.6
GPT-5.3-Codex-Spark
92.7
DeepSeek Coder 2.0
75.5
GPT-5.3-Codex-Spark
78.3
DeepSeek Coder 2.0
59.6
GPT-5.3-Codex-Spark
92
DeepSeek Coder 2.0
86
GPT-5.3-Codex-Spark
90.8
DeepSeek Coder 2.0
79.8
GPT-5.3-Codex-Spark
96.7
DeepSeek Coder 2.0
80.5
GPT-5.3-Codex-Spark is ahead overall, 87 to 66. The biggest single separator in this matchup is MMMU-Pro, where the scores are 86 and 50.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 59.6. 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 52.8. 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 80.5. 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 75.5. 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 67.5. Inside this category, BrowseComp 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 58.6. 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 86. 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 79.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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