Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Sibling matchup inside the GPT-5.2 family.
GPT-5.2 and GPT-5.2 Instant sit in the same GPT-5.2 family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.
GPT-5.2 has the cleaner overall profile here, landing at 88 versus 85. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.2's sharpest advantage is in coding, where it averages 81.8 against 75.5. The single biggest benchmark swing on the page is MRCRv2, 93 to 84. GPT-5.2 Instant does hit back in multilingual, so the answer changes if that is the part of the workload you care about most.
GPT-5.2 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $1.50 input / $6.00 output per 1M tokens for GPT-5.2 Instant. GPT-5.2 gives you the larger context window at 400K, compared with 128K for GPT-5.2 Instant.
GPT-5.2 makes more sense if coding is the priority or you need the larger 400K context window, while GPT-5.2 Instant is the cleaner fit if multilingual is the priority or you want the cheaper token bill.
GPT-5.2
85.4
GPT-5.2 Instant
79.6
GPT-5.2
81.8
GPT-5.2 Instant
75.5
GPT-5.2
95
GPT-5.2 Instant
93.1
GPT-5.2
93.2
GPT-5.2 Instant
90.9
GPT-5.2
79.5
GPT-5.2 Instant
79.8
GPT-5.2
94
GPT-5.2 Instant
95
GPT-5.2
92.4
GPT-5.2 Instant
94.4
GPT-5.2
97.2
GPT-5.2 Instant
97.2
GPT-5.2 and GPT-5.2 Instant are sibling variants in the GPT-5.2 family, so the right pick depends on whether you value the better benchmark line, cheaper tokens, or the larger context window. GPT-5.2 is ahead overall 88 to 85.
GPT-5.2 Instant has the edge for knowledge tasks in this comparison, averaging 79.8 versus 79.5. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for coding in this comparison, averaging 81.8 versus 75.5. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.2 and GPT-5.2 Instant are effectively tied for math here, both landing at 97.2 on average.
GPT-5.2 has the edge for reasoning in this comparison, averaging 93.2 versus 90.9. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 85.4 versus 79.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 95 versus 93.1. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-5.2 Instant has the edge for instruction following in this comparison, averaging 95 versus 94. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.2 Instant has the edge for multilingual tasks in this comparison, averaging 94.4 versus 92.4. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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