Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash
59
GLM-5.1
83
Verified leaderboard positions: DeepSeek V4 Flash #23 · GLM-5.1 #21
Pick GLM-5.1 if you want the stronger benchmark profile. DeepSeek V4 Flash only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Agentic
+16.2 difference
Coding
+3.8 difference
Knowledge
+7.1 difference
DeepSeek V4 Flash
GLM-5.1
$0.14 / $0.28
$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 only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 59. 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 agentic, where it averages 65.3 against 49.1. The single biggest benchmark swing on the page is HLE, 8.1% to 52.3%.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash. That is roughly 15.7x on output cost alone. GLM-5.1 is the reasoning model in the pair, while DeepSeek V4 Flash 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 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 59. The biggest single separator in this matchup is HLE, where the scores are 8.1% and 52.3%.
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 45.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 versus 57.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.3 versus 49.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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