Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
83
Grok 4.20
65
Verified leaderboard positions: GLM-5.1 #21 · Grok 4.20 unranked
Pick GLM-5.1 if you want the stronger benchmark profile. Grok 4.20 only becomes the better choice if coding is the priority or you need the larger 2M context window.
Agentic
+18.2 difference
Coding
+0.1 difference
GLM-5.1
Grok 4.20
$1.4 / $4.4
$2 / $6
N/A
233 t/s
N/A
10.33s
203K
2M
Pick GLM-5.1 if you want the stronger benchmark profile. Grok 4.20 only becomes the better choice if coding is the priority or you need the larger 2M context window.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 47.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 47.1%. Grok 4.20 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
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-5.1. Grok 4.20 gives you the larger context window at 2M, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 47.1%.
Grok 4.20 has the edge for coding in this comparison, averaging 61 versus 60.9. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.3 versus 47.1. 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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