Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5 (Reasoning)
75
LongCat-2.0
80
Pick LongCat-2.0 if you want the stronger benchmark profile. Kimi K2.5 (Reasoning) only becomes the better choice if coding is the priority.
Agentic
+16.2 difference
Coding
+17.3 difference
Kimi K2.5 (Reasoning)
LongCat-2.0
$0.6 / $3
$0.75 / $2.95
N/A
N/A
N/A
N/A
128K
1M
Pick LongCat-2.0 if you want the stronger benchmark profile. Kimi K2.5 (Reasoning) only becomes the better choice if coding is the priority.
LongCat-2.0 is clearly ahead on the provisional aggregate, 80 to 75. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
LongCat-2.0's sharpest advantage is in agentic, where it averages 70.8 against 54.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50.8% to 70.8%. Kimi K2.5 (Reasoning) does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 (Reasoning) is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.75 input / $2.95 output per 1M tokens for LongCat-2.0. LongCat-2.0 gives you the larger context window at 1M, compared with 128K for Kimi K2.5 (Reasoning).
LongCat-2.0 is ahead on BenchLM's provisional leaderboard, 80 to 75. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50.8% and 70.8%.
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 59.5. LongCat-2.0 stays close enough that the answer can still flip depending on your workload.
LongCat-2.0 has the edge for agentic tasks in this comparison, averaging 70.8 versus 54.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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