Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Sonnet 4.6
85
GLM-5
77
Verified leaderboard positions: Claude Sonnet 4.6 unranked · GLM-5 #12
Pick Claude Sonnet 4.6 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if you want the cheaper token bill.
Agentic
+9.1 difference
Coding
+3.2 difference
Knowledge
+3.0 difference
Claude Sonnet 4.6
GLM-5
$3 / $15
$0 / $0
44 t/s
74 t/s
1.48s
1.64s
200K
200K
Pick Claude Sonnet 4.6 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if you want the cheaper token bill.
Claude Sonnet 4.6 is clearly ahead on the provisional aggregate, 85 to 77. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Sonnet 4.6's sharpest advantage is in agentic, where it averages 65.3 against 56.2. The single biggest benchmark swing on the page is SuperGPQA, 95% to 66.8%.
Claude Sonnet 4.6 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-5. That is roughly Infinityx on output cost alone.
Claude Sonnet 4.6 is ahead on BenchLM's provisional leaderboard, 85 to 77. The biggest single separator in this matchup is SuperGPQA, where the scores are 95% and 66.8%.
Claude Sonnet 4.6 has the edge for knowledge tasks in this comparison, averaging 73.7 versus 70.7. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for coding in this comparison, averaging 66.4 versus 63.2. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for agentic tasks in this comparison, averaging 65.3 versus 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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