Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
79
LongCat-2.0
80
Pick LongCat-2.0 if you want the stronger benchmark profile. GPT-5.2 only becomes the better choice if coding is the priority.
Agentic
+15.6 difference
Coding
+5.2 difference
GPT-5.2
LongCat-2.0
$1.75 / $14
$0.75 / $2.95
73 t/s
N/A
130.34s
N/A
400K
1M
Pick LongCat-2.0 if you want the stronger benchmark profile. GPT-5.2 only becomes the better choice if coding is the priority.
LongCat-2.0 finishes one point ahead on BenchLM's provisional leaderboard, 80 to 79. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
LongCat-2.0's sharpest advantage is in agentic, where it averages 70.8 against 55.2. The single biggest benchmark swing on the page is SWE-bench Pro, 55.6% to 59.5%. GPT-5.2 does hit back in coding, 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 $1.75 input / $14.00 output per 1M tokens, versus $0.75 input / $2.95 output per 1M tokens for LongCat-2.0. That is roughly 4.7x on output cost alone. LongCat-2.0 gives you the larger context window at 1M, compared with 400K for GPT-5.2.
LongCat-2.0 is ahead on BenchLM's provisional leaderboard, 80 to 79. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 55.6% and 59.5%.
GPT-5.2 has the edge for coding in this comparison, averaging 64.7 versus 59.5. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
LongCat-2.0 has the edge for agentic tasks in this comparison, averaging 70.8 versus 55.2. GPT-5.2 stays close enough that the answer can still flip depending on your workload.
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