Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
74
LongCat-2.0
80
Verified leaderboard positions: GLM-5.1 #30 · LongCat-2.0 unranked
Pick LongCat-2.0 if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if coding is the priority.
Agentic
+5.5 difference
Coding
+1.4 difference
GLM-5.1
LongCat-2.0
$1.4 / $4.4
$0.75 / $2.95
N/A
N/A
N/A
N/A
203K
1M
Pick LongCat-2.0 if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if coding is the priority.
LongCat-2.0 is clearly ahead on the provisional aggregate, 80 to 74. 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 65.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 70.8%. GLM-5.1 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 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 203K for GLM-5.1.
LongCat-2.0 is ahead on BenchLM's provisional leaderboard, 80 to 74. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 70.8%.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 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 65.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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