Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro (High)
83
GLM-5.1
82
Verified leaderboard positions: DeepSeek V4 Pro (High) #10 · GLM-5.1 #25
Pick DeepSeek V4 Pro (High) if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Agentic
+4.7 difference
Coding
+12.9 difference
Knowledge
+10.3 difference
DeepSeek V4 Pro (High)
GLM-5.1
$1.74 / $3.48
$1.4 / $4.4
N/A
N/A
N/A
N/A
1M
203K
Pick DeepSeek V4 Pro (High) if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
DeepSeek V4 Pro (High) finishes one point ahead on BenchLM's provisional leaderboard, 83 to 82. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
DeepSeek V4 Pro (High)'s sharpest advantage is in coding, where it averages 73.8 against 60.9. The single biggest benchmark swing on the page is HLE, 34.5% 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 $1.74 input / $3.48 output per 1M tokens for DeepSeek V4 Pro (High). DeepSeek V4 Pro (High) gives you the larger context window at 1M, compared with 203K for GLM-5.1.
DeepSeek V4 Pro (High) is ahead on BenchLM's provisional leaderboard, 83 to 82. The biggest single separator in this matchup is HLE, where the scores are 34.5% and 52.3%.
DeepSeek V4 Pro (High) has the edge for knowledge tasks in this comparison, averaging 62.6 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
DeepSeek V4 Pro (High) has the edge for coding in this comparison, averaging 73.8 versus 60.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
DeepSeek V4 Pro (High) has the edge for agentic tasks in this comparison, averaging 70 versus 65.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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