Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
Muse Spark
81
Verified leaderboard positions: GLM-5.1 #25 · Muse Spark unranked
Pick GLM-5.1 if you want the stronger benchmark profile. Muse Spark only becomes the better choice if coding is the priority or you need the larger 262K context window.
Agentic
+6.3 difference
Coding
+0.8 difference
Knowledge
+1.9 difference
GLM-5.1
Muse Spark
$1.4 / $4.4
N/A
N/A
N/A
N/A
N/A
203K
262K
Pick GLM-5.1 if you want the stronger benchmark profile. Muse Spark only becomes the better choice if coding is the priority or you need the larger 262K context window.
GLM-5.1 finishes one point ahead on BenchLM's provisional leaderboard, 82 to 81. 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.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 59. The single biggest benchmark swing on the page is SWE-bench Pro, 58.4% to 52.4%. Muse Spark does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Muse Spark gives you the larger context window at 262K, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 82 to 81. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 58.4% and 52.4%.
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 50.4. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Muse Spark has the edge for coding in this comparison, averaging 61.7 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 59. Inside this category, CyberGym is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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