Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
31
GLM-5.1
83
Verified leaderboard positions: DeepSeek V4 Flash Base unranked · GLM-5.1 #21
Pick GLM-5.1 if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+0.1 difference
DeepSeek V4 Flash Base
GLM-5.1
$null / $null
$1.4 / $4.4
N/A
N/A
N/A
N/A
1M
203K
Pick GLM-5.1 if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if you need the larger 1M context window 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 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in knowledge, where it averages 52.3 against 52.2.
GLM-5.1 is the reasoning model in the pair, while DeepSeek V4 Flash Base 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. DeepSeek V4 Flash Base gives you the larger context window at 1M, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 31.
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 52.2. DeepSeek V4 Flash Base stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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