Noometry

Model comparison

GLM-4.6 vs MiniMax-M2.7

GLM-4.6 is the stronger model overall, scoring 40.9 to 37.4 on the Noometry Index. MiniMax-M2.7 costs 1.9× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 23 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

40.9

Rank #132 Confirmed

MiniMax-M2.7 MiniMax

37.4

Rank #188 Confirmed

Summary

  • They share 23 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and MiniMax-M2.7 in 1 category; 7 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.6 leads 36.4 to 24.9.
  • The biggest single-benchmark swing is Terminal-Bench: 24.5% for GLM-4.6 and 45.1% for MiniMax-M2.7.
  • MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.

Side by side

GLM-4.6MiniMax-M2.7
ProviderZ.ai (Zhipu)MiniMax
Noometry Index40.937.4
Released2025-09-302026-03-18
WeightsOpenOpen
Context window205K205K
Max output131K131K
Input $ / M tokens$0.60$0.30
Output $ / M tokens$2.20$1.20
Results tracked2930

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Category by category

Coding MiniMax-M2.7 leads

GLM-4.6: 39.9 (#137), MiniMax-M2.7: 41.4 (#115)

BenchmarkGLM-4.6MiniMax-M2.7
LMArena WebDev13391397
SciCode38.4%47%
LMArena Coding14491454
ALE-Bench340.82599.25
SWE-bench Verified (bash only)55.4%—
WeirdML—37%

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.1 (#60), MiniMax-M2.7: 24.9 (#103)

BenchmarkGLM-4.6MiniMax-M2.7
Terminal-Bench24.5%45.1%
Berkeley Function Calling Leaderboard72.4%—
ExploitBench—13.3%
GBAEval—0%

Reasoning GLM-4.6 leads

GLM-4.6: 23.1 (#168), MiniMax-M2.7: 19.0 (#248)

BenchmarkGLM-4.6MiniMax-M2.7
CritPt1.1%0.6%
LMArena Hard Prompts14401422
Kagi LLM Benchmark47.4%—
NYT Connections (extended)—24.7%
Thematic Generalization—39.3%
Epoch Capabilities Index—145.84

Math GLM-4.6 leads

GLM-4.6: 36.4 (#115), MiniMax-M2.7: 24.9 (#254)

BenchmarkGLM-4.6MiniMax-M2.7
LMArena Math14311420
ProofBench—3%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 39.6 (#118), MiniMax-M2.7: 37.1 (#151)

BenchmarkGLM-4.6MiniMax-M2.7
Vectara Hallucination Rate9.5%12.9%
LMArena Expert14321443

Multilingual GLM-4.6 leads

GLM-4.6: 53.6 (#62), MiniMax-M2.7: 50.5 (#120)

BenchmarkGLM-4.6MiniMax-M2.7
LMArena Non-English14261382
LMArena Chinese15001439
LMArena French14591422
LMArena German14471398
LMArena Japanese13941261
LMArena Korean14011313
LMArena Russian14181383
LMArena Spanish14361403

Instruction Following Too close to call

GLM-4.6: 74.5 (#93), MiniMax-M2.7: 74.2 (#101)

BenchmarkGLM-4.6MiniMax-M2.7
LMArena Instruction Following14101404

Long Context Too close to call

GLM-4.6: 43.4 (#91), MiniMax-M2.7: 43.3 (#94)

BenchmarkGLM-4.6MiniMax-M2.7
LMArena Longer Query14231419

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.3 (#86), MiniMax-M2.7: 59.1 (#107)

BenchmarkGLM-4.6MiniMax-M2.7
LMArena Text14401404
LMArena Creative Writing14111354
LMArena Multi-Turn14271413
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than MiniMax-M2.7?

GLM-4.6 is the stronger model overall, scoring 40.9 to 37.4 on the Noometry Index. MiniMax-M2.7 costs 1.9× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Which is cheaper, GLM-4.6 or MiniMax-M2.7?

MiniMax-M2.7 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or MiniMax-M2.7 better for coding?

MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 39.9 in the Noometry coding category.

Which has the bigger context window?

Both accept 205K tokens.

How many benchmarks do GLM-4.6 and MiniMax-M2.7 share?

23 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiniMax-M2.7 has 30.

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