Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
69
Grok 4.3
79
Pick Grok 4.3 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.
Coding
+23.3 difference
Knowledge
+6.7 difference
GLM-4.7
Grok 4.3
$0 / $0
$1.25 / $2.5
82 t/s
209 t/s
1.10s
12.36s
200K
1M
Pick Grok 4.3 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 4.3 is clearly ahead on the provisional aggregate, 79 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Grok 4.3 is also the more expensive model on tokens at $1.25 input / $2.50 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 4.3 gives you the larger context window at 1M, compared with 200K for GLM-4.7.
Grok 4.3 is ahead on BenchLM's provisional leaderboard, 79 to 69. The biggest single separator in this matchup is HLE, where the scores are 24.8% and 35%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 53.9. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 47.3. Grok 4.3 stays close enough that the answer can still flip depending on your workload.
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