Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3 Codex is clearly ahead on the aggregate, 89 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in coding, where it averages 87.3 against 41.1. The single biggest benchmark swing on the page is LiveCodeBench, 85 to 38.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 13.3x on output cost alone. GPT-5.3 Codex gives you the larger context window at 400K, compared with 128K for Mercury 2.
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Mercury 2 only becomes the better choice if you want the cheaper token bill.
GPT-5.3 Codex
88.1
Mercury 2
63.7
GPT-5.3 Codex
87.3
Mercury 2
41.1
GPT-5.3 Codex
91.3
Mercury 2
68.3
GPT-5.3 Codex
93.7
Mercury 2
80.1
GPT-5.3 Codex
80.3
Mercury 2
57.2
GPT-5.3 Codex
93
Mercury 2
84
GPT-5.3 Codex
92.8
Mercury 2
79.7
GPT-5.3 Codex
97.7
Mercury 2
80.9
GPT-5.3 Codex is ahead overall, 89 to 65. The biggest single separator in this matchup is LiveCodeBench, where the scores are 85 and 38.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 80.3 versus 57.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 87.3 versus 41.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for math in this comparison, averaging 97.7 versus 80.9. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for reasoning in this comparison, averaging 93.7 versus 80.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 88.1 versus 63.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multimodal and grounded tasks in this comparison, averaging 91.3 versus 68.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for instruction following in this comparison, averaging 93 versus 84. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for multilingual tasks in this comparison, averaging 92.8 versus 79.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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