Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Opus 4.7 (Adaptive)
86
GLM-5.1
83
Verified leaderboard positions: Claude Opus 4.7 (Adaptive) #8 · GLM-5.1 #21
Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if you want the cheaper token bill.
Agentic
+9.6 difference
Coding
+12.0 difference
Knowledge
+15.9 difference
Claude Opus 4.7 (Adaptive)
GLM-5.1
$5 / $25
$1.4 / $4.4
N/A
N/A
N/A
N/A
1M
203K
Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if you want the cheaper token bill.
Claude Opus 4.7 (Adaptive) has the cleaner provisional overall profile here, landing at 86 versus 83. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Claude Opus 4.7 (Adaptive)'s sharpest advantage is in knowledge, where it averages 68.2 against 52.3. The single biggest benchmark swing on the page is BrowseComp, 79.3% to 68%.
Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.1. That is roughly 5.7x on output cost alone. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 203K for GLM-5.1.
Claude Opus 4.7 (Adaptive) is ahead on BenchLM's provisional leaderboard, 86 to 83. The biggest single separator in this matchup is BrowseComp, where the scores are 79.3% and 68%.
Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 68.2 versus 52.3. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 72.9 versus 60.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 74.9 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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