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 77. 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 54.1. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 49. Gemini 3 Pro does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Gemini 3 Pro 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. Gemini 3 Pro gives you the larger context window at 2M, compared with 256K for GPT-5.3-Codex-Spark.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Gemini 3 Pro only becomes the better choice if multimodal & grounded is the priority or you need the larger 2M context window.
GPT-5.3-Codex-Spark
85.6
Gemini 3 Pro
71.1
GPT-5.3-Codex-Spark
82.3
Gemini 3 Pro
54.1
GPT-5.3-Codex-Spark
88.3
Gemini 3 Pro
93.1
GPT-5.3-Codex-Spark
92.7
Gemini 3 Pro
91.2
GPT-5.3-Codex-Spark
78.3
Gemini 3 Pro
71.8
GPT-5.3-Codex-Spark
92
Gemini 3 Pro
88
GPT-5.3-Codex-Spark
90.8
Gemini 3 Pro
86.4
GPT-5.3-Codex-Spark
96.7
Gemini 3 Pro
94.1
GPT-5.3-Codex-Spark is ahead overall, 87 to 77. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 49.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 71.8. 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 54.1. 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 94.1. Inside this category, MATH-500 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 91.2. Inside this category, BBH 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 71.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Gemini 3 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 93.1 versus 88.3. 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 88. 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 86.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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