Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
57
GLM-4.7
68
Pick GLM-4.7 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+9.7 difference
DeepSeek V3.2
GLM-4.7
$0.28 / $0.42
$0 / $0
35 t/s
82 t/s
3.75s
1.10s
128K
200K
Pick GLM-4.7 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7 is clearly ahead on the provisional aggregate, 68 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 60.9. The single biggest benchmark swing on the page is SWE-Rebench, 60.9% to 58.7%.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 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. GLM-4.7 is the reasoning model in the pair, while DeepSeek V3.2 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. GLM-4.7 gives you the larger context window at 200K, compared with 128K for DeepSeek V3.2.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 68 to 57. The biggest single separator in this matchup is SWE-Rebench, where the scores are 60.9% and 58.7%.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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