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 61. 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 44.2. The single biggest benchmark swing on the page is SWE-bench Pro, 90 to 46.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron 3 Super 120B A12B. That is roughly Infinityx on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while Nemotron 3 Super 120B A12B 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 gives you the larger context window at 400K, compared with 256K for Nemotron 3 Super 120B A12B.
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Nemotron 3 Super 120B A12B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3 Codex
88.1
Nemotron 3 Super 120B A12B
55.3
GPT-5.3 Codex
87.3
Nemotron 3 Super 120B A12B
44.2
GPT-5.3 Codex
91.3
Nemotron 3 Super 120B A12B
60.4
GPT-5.3 Codex
93.7
Nemotron 3 Super 120B A12B
71.8
GPT-5.3 Codex
80.3
Nemotron 3 Super 120B A12B
55.8
GPT-5.3 Codex
93
Nemotron 3 Super 120B A12B
86
GPT-5.3 Codex
92.8
Nemotron 3 Super 120B A12B
81.5
GPT-5.3 Codex
97.7
Nemotron 3 Super 120B A12B
74.6
GPT-5.3 Codex is ahead overall, 89 to 61. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 90 and 46.
GPT-5.3 Codex has the edge for knowledge tasks in this comparison, averaging 80.3 versus 55.8. Inside this category, MMLU 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 44.2. Inside this category, SWE-bench Pro 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 74.6. 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 71.8. Inside this category, SimpleQA 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 55.3. 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 60.4. 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 86. 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 81.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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