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 65. 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 41.1. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 38.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.25 input / $0.75 output per 1M tokens for Mercury 2. That is roughly 10.7x on output cost alone. GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 128K for Mercury 2.
Pick GPT-5.3-Codex-Spark 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-Spark
85.6
Mercury 2
63.7
GPT-5.3-Codex-Spark
82.3
Mercury 2
41.1
GPT-5.3-Codex-Spark
88.3
Mercury 2
68.3
GPT-5.3-Codex-Spark
92.7
Mercury 2
80.1
GPT-5.3-Codex-Spark
78.3
Mercury 2
57.2
GPT-5.3-Codex-Spark
92
Mercury 2
84
GPT-5.3-Codex-Spark
90.8
Mercury 2
79.7
GPT-5.3-Codex-Spark
96.7
Mercury 2
80.9
GPT-5.3-Codex-Spark is ahead overall, 87 to 65. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 38.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 57.2. 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 41.1. 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 80.9. 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 80.1. 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 63.7. 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 68.3. 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 84. 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 79.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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