Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
Grok 4.20
73
Verified leaderboard positions: GLM-5 #17 · Grok 4.20 unranked
Pick Grok 4.20 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+9.1 difference
Coding
+2.2 difference
Reasoning
+7.5 difference
GLM-5
Grok 4.20
$1 / $3.2
$2 / $6
74 t/s
233 t/s
1.64s
10.33s
200K
2M
Pick Grok 4.20 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Grok 4.20 is clearly ahead on the provisional aggregate, 73 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Grok 4.20 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. Grok 4.20 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. Grok 4.20 gives you the larger context window at 2M, compared with 200K for GLM-5.
Grok 4.20 is ahead on BenchLM's provisional leaderboard, 73 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 47.1%.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 61. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GLM-5 has the edge for reasoning in this comparison, averaging 60.8 versus 53.3. Grok 4.20 stays close enough that the answer can still flip depending on your workload.
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 47.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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