Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.2-Codex is clearly ahead on the aggregate, 85 to 61. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2-Codex's sharpest advantage is in coding, where it averages 76 against 44.2. The single biggest benchmark swing on the page is SWE-bench Pro, 86 to 46.
GPT-5.2-Codex is also the more expensive model on tokens at $2.00 input / $8.00 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-5.2-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-5.2-Codex gives you the larger context window at 400K, compared with 256K for Nemotron 3 Super 120B A12B.
Pick GPT-5.2-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-5.2-Codex
87
Nemotron 3 Super 120B A12B
55.3
GPT-5.2-Codex
76
Nemotron 3 Super 120B A12B
44.2
GPT-5.2-Codex
87.6
Nemotron 3 Super 120B A12B
60.4
GPT-5.2-Codex
92
Nemotron 3 Super 120B A12B
71.8
GPT-5.2-Codex
72.5
Nemotron 3 Super 120B A12B
55.8
GPT-5.2-Codex
92
Nemotron 3 Super 120B A12B
86
GPT-5.2-Codex
88.4
Nemotron 3 Super 120B A12B
81.5
GPT-5.2-Codex
95.4
Nemotron 3 Super 120B A12B
74.6
GPT-5.2-Codex is ahead overall, 85 to 61. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 86 and 46.
GPT-5.2-Codex has the edge for knowledge tasks in this comparison, averaging 72.5 versus 55.8. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for coding in this comparison, averaging 76 versus 44.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for math in this comparison, averaging 95.4 versus 74.6. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for reasoning in this comparison, averaging 92 versus 71.8. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for agentic tasks in this comparison, averaging 87 versus 55.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for multimodal and grounded tasks in this comparison, averaging 87.6 versus 60.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for instruction following in this comparison, averaging 92 versus 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2-Codex has the edge for multilingual tasks in this comparison, averaging 88.4 versus 81.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.