Noometry

Model comparison

GLM-4.6 vs MiniMax-M2

GLM-4.6 is the stronger model overall, scoring 41.4 to 37.4 on the Noometry Index. MiniMax-M2 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 . 19 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

MiniMax-M2 MiniMax

37.4

Rank #204 Confirmed

Summary

  • They share 19 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and MiniMax-M2 in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in multilingual, where GLM-4.6 leads 53.5 to 45.3.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 57.8% for MiniMax-M2.
  • MiniMax-M2 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
ProviderZ.ai (Zhipu)MiniMax
Noometry Index41.437.4
Released2025-09-302025-10-27
WeightsOpenOpen
Context window205K205K
Max output131K131K
Input $ / M tokens$0.60$0.30
Output $ / M tokens$2.20$1.20
Results tracked2921

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Category by category

Coding Too close to call

GLM-4.6: 40.1 (#148), MiniMax-M2: 39.3 (#159)

BenchmarkGLM-4.6MiniMax-M2
SWE-bench Verified (bash only)55.4%61%
LMArena WebDev13401297
LMArena Coding14491370
SciCode38.4%—
ALE-Bench340.82—

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.3 (#66), MiniMax-M2: 25.1 (#109)

BenchmarkGLM-4.6MiniMax-M2
Terminal-Bench24.5%30%
Berkeley Function Calling Leaderboard72.4%—
Vending-Bench 2—160.6

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), MiniMax-M2: 19.4 (#258)

BenchmarkGLM-4.6MiniMax-M2
Kagi LLM Benchmark47.4%57.8%
LMArena Hard Prompts14401357
NYT Connections (extended)—14.8%
CritPt1.1%—

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), MiniMax-M2: 37.3 (#160)

BenchmarkGLM-4.6MiniMax-M2
LMArena Math14321352
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), MiniMax-M2: 37.0 (#163)

BenchmarkGLM-4.6MiniMax-M2
LMArena Expert14311337
Vectara Hallucination Rate9.5%—

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), MiniMax-M2: 45.3 (#171)

BenchmarkGLM-4.6MiniMax-M2
LMArena Non-English14261313
LMArena Chinese14991366
LMArena French14591335
LMArena German14471355
LMArena Russian14191331
LMArena Spanish14361326
LMArena Japanese1393—
LMArena Korean1400—

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), MiniMax-M2: 70.2 (#166)

BenchmarkGLM-4.6MiniMax-M2
LMArena Instruction Following14101328

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), MiniMax-M2: 40.5 (#153)

BenchmarkGLM-4.6MiniMax-M2
LMArena Longer Query14221331

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), MiniMax-M2: 53.0 (#162)

BenchmarkGLM-4.6MiniMax-M2
LMArena Text14401340
LMArena Creative Writing14111286
LMArena Multi-Turn14271361
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than MiniMax-M2?

GLM-4.6 is the stronger model overall, scoring 41.4 to 37.4 on the Noometry Index. MiniMax-M2 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?

MiniMax-M2 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 better for coding?

They score almost the same on coding (40.1 vs 39.3); test both on your own repository before choosing.

Which has the bigger context window?

Both accept 205K tokens.

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

19 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiniMax-M2 has 21.

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