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 46. 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 13.1. The single biggest benchmark swing on the page is SWE-bench Verified, 80 to 5.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Gemini 1.0 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. GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 32K for Gemini 1.0 Pro.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Gemini 1.0 Pro 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
Gemini 1.0 Pro
39.8
GPT-5.3-Codex-Spark
82.3
Gemini 1.0 Pro
13.1
GPT-5.3-Codex-Spark
88.3
Gemini 1.0 Pro
68.1
GPT-5.3-Codex-Spark
92.7
Gemini 1.0 Pro
57.4
GPT-5.3-Codex-Spark
78.3
Gemini 1.0 Pro
42.8
GPT-5.3-Codex-Spark
92
Gemini 1.0 Pro
77
GPT-5.3-Codex-Spark
90.8
Gemini 1.0 Pro
66.8
GPT-5.3-Codex-Spark
96.7
Gemini 1.0 Pro
65.9
GPT-5.3-Codex-Spark is ahead overall, 87 to 46. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 80 and 5.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 42.8. 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 13.1. Inside this category, SWE-bench Verified 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 65.9. 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 57.4. Inside this category, LongBench v2 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 39.8. 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 68.1. 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 77. 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 66.8. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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