Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
58
GLM-5.1
83
Verified leaderboard positions: DeepSeek V3.2 unranked · GLM-5.1 #21
Pick GLM-5.1 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
DeepSeek V3.2
GLM-5.1
$0.28 / $0.42
$1.4 / $4.4
35 t/s
N/A
3.75s
N/A
128K
203K
Pick GLM-5.1 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 10.5x on output cost alone. GLM-5.1 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-5.1 gives you the larger context window at 203K, compared with 128K for DeepSeek V3.2.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 58. The biggest single separator in this matchup is SWE-Rebench, where the scores are 60.9% and 62.7%.
DeepSeek V3.2 and GLM-5.1 are effectively tied for coding here, both landing at 60.9 on average.
Estimates at 50,000 req/day · 1000 tokens/req average.
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