Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
81
Grok 4.3
79
Pick GPT-5.2 if you want the stronger benchmark profile. Grok 4.3 only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Coding
+17.4 difference
Knowledge
+38.5 difference
Multimodal
+2.2 difference
GPT-5.2
Grok 4.3
$1.75 / $14
$1.25 / $2.5
73 t/s
209 t/s
130.34s
12.36s
400K
1M
Pick GPT-5.2 if you want the stronger benchmark profile. Grok 4.3 only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
GPT-5.2 has the cleaner provisional overall profile here, landing at 81 versus 79. 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 knowledge, where it averages 92.4 against 53.9. The single biggest benchmark swing on the page is GPQA, 92.4% to 90.1%.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $1.25 input / $2.50 output per 1M tokens for Grok 4.3. That is roughly 5.6x on output cost alone. Grok 4.3 gives you the larger context window at 1M, compared with 400K for GPT-5.2.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 81 to 79. The biggest single separator in this matchup is GPQA, where the scores are 92.4% and 90.1%.
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 53.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for coding in this comparison, averaging 64.7 versus 47.3. Grok 4.3 stays close enough that the answer can still flip depending on your workload.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 80.3 versus 78.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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