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 63. 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 41.4. The single biggest benchmark swing on the page is SWE-bench Pro, 86 to 45.
GPT-5.2-Codex is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.25 input / $2.00 output per 1M tokens for Seed-2.0-Lite. That is roughly 4.0x on output cost alone. GPT-5.2-Codex is the reasoning model in the pair, while Seed-2.0-Lite 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 Seed-2.0-Lite.
Pick GPT-5.2-Codex if you want the stronger benchmark profile. Seed-2.0-Lite 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
Seed-2.0-Lite
55.1
GPT-5.2-Codex
76
Seed-2.0-Lite
41.4
GPT-5.2-Codex
87.6
Seed-2.0-Lite
79.6
GPT-5.2-Codex
92
Seed-2.0-Lite
73
GPT-5.2-Codex
72.5
Seed-2.0-Lite
53.9
GPT-5.2-Codex
92
Seed-2.0-Lite
89
GPT-5.2-Codex
88.4
Seed-2.0-Lite
82.5
GPT-5.2-Codex
95.4
Seed-2.0-Lite
75
GPT-5.2-Codex is ahead overall, 85 to 63. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 86 and 45.
GPT-5.2-Codex has the edge for knowledge tasks in this comparison, averaging 72.5 versus 53.9. 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 41.4. 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 75. 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 73. 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 55.1. 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 79.6. Inside this category, OfficeQA 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 89. 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 82.5. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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