Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
69
Grok 4.20
73
Pick Grok 4.20 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+1.8 difference
Coding
+9.6 difference
GLM-4.7
Grok 4.20
$0 / $0
$2 / $6
82 t/s
233 t/s
1.10s
10.33s
200K
2M
Pick Grok 4.20 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Grok 4.20 is clearly ahead on the provisional aggregate, 73 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Grok 4.20's sharpest advantage is in agentic, where it averages 47.1 against 45.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 47.1%. GLM-4.7 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 $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. Grok 4.20 gives you the larger context window at 2M, compared with 200K for GLM-4.7.
Grok 4.20 is ahead on BenchLM's provisional leaderboard, 73 to 69. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 47.1%.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 61. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Grok 4.20 has the edge for agentic tasks in this comparison, averaging 47.1 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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