Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
MiniMax M3
76
Verified leaderboard positions: GLM-5.1 #25 · MiniMax M3 #12
Pick GLM-5.1 if you want the stronger benchmark profile. MiniMax M3 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+6.6 difference
Coding
+6.1 difference
GLM-5.1
MiniMax M3
$1.4 / $4.4
$0.3 / $1.2
N/A
N/A
N/A
N/A
203K
1M
Pick GLM-5.1 if you want the stronger benchmark profile. MiniMax M3 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
GLM-5.1 is clearly ahead on the provisional aggregate, 82 to 76. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. That is roughly 3.7x on output cost alone. GLM-5.1 is the reasoning model in the pair, while MiniMax M3 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. MiniMax M3 gives you the larger context window at 1M, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 82 to 76. The biggest single separator in this matchup is BrowseComp, where the scores are 68% and 83.5%.
MiniMax M3 has the edge for coding in this comparison, averaging 67 versus 60.9. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiniMax M3 has the edge for agentic tasks in this comparison, averaging 71.9 versus 65.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.