Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
Grok 4.20
71
Verified leaderboard positions: GLM-5.2 #9 · Grok 4.20 unranked
Pick GLM-5.2 if you want the stronger benchmark profile. Grok 4.20 only becomes the better choice if you need the larger 2M context window.
Agentic
+33.9 difference
Coding
+1.1 difference
GLM-5.2
Grok 4.20
$1.4 / $4.4
$2 / $6
N/A
233 t/s
N/A
10.33s
1M
2M
Pick GLM-5.2 if you want the stronger benchmark profile. Grok 4.20 only becomes the better choice if you need the larger 2M context window.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in agentic, where it averages 81 against 47.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 47.1%.
Grok 4.20 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. Grok 4.20 gives you the larger context window at 2M, compared with 1M for GLM-5.2.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 47.1%.
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 61. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 47.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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