Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
Laguna XS.2
32
Verified leaderboard positions: GLM-5 #17 · Laguna XS.2 unranked
Pick GLM-5 if you want the stronger benchmark profile. Laguna XS.2 only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Agentic
+26.1 difference
Coding
+9.9 difference
GLM-5
Laguna XS.2
$1 / $3.2
$0 / $0
74 t/s
N/A
1.64s
N/A
200K
131K
Pick GLM-5 if you want the stronger benchmark profile. Laguna XS.2 only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 32. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in agentic, where it averages 56.2 against 30.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 30.1%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Laguna XS.2. That is roughly Infinityx on output cost alone. Laguna XS.2 is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GLM-5 gives you the larger context window at 200K, compared with 131K for Laguna XS.2.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 32. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 30.1%.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 53.3. Inside this category, SWE Multilingual is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 30.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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