Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Sonnet 4.6
83
GLM-5.1
83
Verified leaderboard positions: Claude Sonnet 4.6 unranked · GLM-5.1 #21
Treat this as a split decision. Claude Sonnet 4.6 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; GLM-5.1 is the better fit if agentic is the priority or you want the cheaper token bill.
Agentic
+0.2 difference
Coding
+5.5 difference
Knowledge
+21.4 difference
Claude Sonnet 4.6
GLM-5.1
$3 / $15
$1.4 / $4.4
44 t/s
N/A
1.48s
N/A
200K
203K
Treat this as a split decision. Claude Sonnet 4.6 makes more sense if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model; GLM-5.1 is the better fit if agentic is the priority or you want the cheaper token bill.
Claude Sonnet 4.6 and GLM-5.1 finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
Claude Sonnet 4.6 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.1. That is roughly 3.4x on output cost alone. GLM-5.1 is the reasoning model in the pair, while Claude Sonnet 4.6 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. GLM-5.1 gives you the larger context window at 203K, compared with 200K for Claude Sonnet 4.6.
Claude Sonnet 4.6 and GLM-5.1 are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Claude Sonnet 4.6 has the edge for knowledge tasks in this comparison, averaging 73.7 versus 52.3. Inside this category, HLE 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 60.9. Inside this category, Vibe Code Bench 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 65.1. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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