Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M2.7
59
Muse Spark
69
Pick Muse Spark if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+2.0 difference
Coding
+5.5 difference
MiniMax M2.7
Muse Spark
$0.3 / $1.2
N/A
45 t/s
N/A
2.53s
N/A
200K
262K
Pick Muse Spark if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Muse Spark is clearly ahead on the provisional aggregate, 69 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Muse Spark's sharpest advantage is in coding, where it averages 61.7 against 56.2. The single biggest benchmark swing on the page is GDPval-AA, 1495 to 1444.
Muse Spark is the reasoning model in the pair, while MiniMax M2.7 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. Muse Spark gives you the larger context window at 262K, compared with 200K for MiniMax M2.7.
Muse Spark is ahead on BenchLM's provisional leaderboard, 69 to 59. The biggest single separator in this matchup is GDPval-AA, where the scores are 1495 and 1444.
Muse Spark has the edge for coding in this comparison, averaging 61.7 versus 56.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Muse Spark has the edge for agentic tasks in this comparison, averaging 59 versus 57. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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