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 48. 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 33.7. The single biggest benchmark swing on the page is HumanEval, 91 to 41.
GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 32K for Nemotron Ultra 253B.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Nemotron Ultra 253B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-5.3-Codex-Spark
85.6
Nemotron Ultra 253B
46.7
GPT-5.3-Codex-Spark
82.3
Nemotron Ultra 253B
33.7
GPT-5.3-Codex-Spark
88.3
Nemotron Ultra 253B
44.7
GPT-5.3-Codex-Spark
92.7
Nemotron Ultra 253B
53.3
GPT-5.3-Codex-Spark
78.3
Nemotron Ultra 253B
42.4
GPT-5.3-Codex-Spark
92
Nemotron Ultra 253B
78
GPT-5.3-Codex-Spark
90.8
Nemotron Ultra 253B
70.1
GPT-5.3-Codex-Spark
96.7
Nemotron Ultra 253B
59.7
GPT-5.3-Codex-Spark is ahead overall, 87 to 48. The biggest single separator in this matchup is HumanEval, where the scores are 91 and 41.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 42.4. Inside this category, MMLU 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 33.7. Inside this category, HumanEval 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 59.7. 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 53.3. Inside this category, SimpleQA 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 46.7. 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 44.7. 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 78. 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 70.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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