Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark has the cleaner overall profile here, landing at 87 versus 84. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.3-Codex-Spark's sharpest advantage is in coding, where it averages 82.3 against 71.9. The single biggest benchmark swing on the page is OSWorld-Verified, 83 to 68. Gemini 3.1 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 also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $1.25 input / $5.00 output per 1M tokens for Gemini 3.1 Pro. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Gemini 3.1 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 3.1 Pro gives you the larger context window at 1M, compared with 256K for GPT-5.3-Codex-Spark.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Gemini 3.1 Pro only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
GPT-5.3-Codex-Spark
85.6
Gemini 3.1 Pro
76.1
GPT-5.3-Codex-Spark
82.3
Gemini 3.1 Pro
71.9
GPT-5.3-Codex-Spark
88.3
Gemini 3.1 Pro
95
GPT-5.3-Codex-Spark
92.7
Gemini 3.1 Pro
92.7
GPT-5.3-Codex-Spark
78.3
Gemini 3.1 Pro
79.4
GPT-5.3-Codex-Spark
92
Gemini 3.1 Pro
95
GPT-5.3-Codex-Spark
90.8
Gemini 3.1 Pro
94.1
GPT-5.3-Codex-Spark
96.7
Gemini 3.1 Pro
96.8
GPT-5.3-Codex-Spark is ahead overall, 87 to 84. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 83 and 68.
Gemini 3.1 Pro has the edge for knowledge tasks in this comparison, averaging 79.4 versus 78.3. Inside this category, MMLU-Pro 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 71.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for math in this comparison, averaging 96.8 versus 96.7. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark and Gemini 3.1 Pro are effectively tied for reasoning here, both landing at 92.7 on average.
GPT-5.3-Codex-Spark has the edge for agentic tasks in this comparison, averaging 85.6 versus 76.1. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 88.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for instruction following in this comparison, averaging 95 versus 92. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Gemini 3.1 Pro has the edge for multilingual tasks in this comparison, averaging 94.1 versus 90.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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