Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
73
Muse Spark
69
Pick GLM-4.7 if you want the stronger benchmark profile. Muse Spark only becomes the better choice if agentic is the priority or you need the larger 262K context window.
Agentic
+13.7 difference
Coding
+19.1 difference
Knowledge
+10.2 difference
GLM-4.7
Muse Spark
$0 / $0
N/A
82 t/s
N/A
1.10s
N/A
200K
262K
Pick GLM-4.7 if you want the stronger benchmark profile. Muse Spark only becomes the better choice if agentic is the priority or you need the larger 262K context window.
GLM-4.7 is clearly ahead on the provisional aggregate, 73 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 80.8 against 61.7. The single biggest benchmark swing on the page is HLE, 24.8% to 50.4%. Muse Spark does hit back in agentic, 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 200K for GLM-4.7.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 73 to 69. The biggest single separator in this matchup is HLE, where the scores are 24.8% and 50.4%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 50.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 80.8 versus 61.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Muse Spark has the edge for agentic tasks in this comparison, averaging 59 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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