Noometry

Model comparison

GLM-4.6V vs MiniMax-M2.5

GLM-4.6V is the stronger model overall, scoring 40.9 to 38.1 on the Noometry Index.

Last verified . 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

MiniMax-M2.5 MiniMax

38.1

Rank #181 Confirmed

Summary

  • They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 4 categories and MiniMax-M2.5 in 3 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-4.6V leads 26.5 to 17.6.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.5.
  • MiniMax-M2.5 accepts more context: 205K tokens versus 128K.

Side by side

GLM-4.6VMiniMax-M2.5
ProviderZ.ai (Zhipu)MiniMax
Noometry Index40.938.1
Released2025-12-082026-02-12
WeightsOpenOpen
Context window128K205K
Max output33K131K
Input $ / M tokens$0.30$0.30
Output $ / M tokens$0.90$1.20
Results tracked1233

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Category by category

Coding MiniMax-M2.5 leads

GLM-4.6V: 40.2 (#128), MiniMax-M2.5: 47.9 (#53)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Coding13901380
SWE-bench Verified (bash only)—75.8%
LMArena WebDev—1386
SWE-bench Multilingual—68.3%
ALE-Bench—618.17

Agentic & Tool Use Not comparable

GLM-4.6V: —, MiniMax-M2.5: 30.2 (#70)

BenchmarkGLM-4.6VMiniMax-M2.5
Terminal-Bench—42.7%
Vending-Bench 2—-23.16

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), MiniMax-M2.5: 17.6 (#281)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Hard Prompts13681372
ARC-AGI-2—4.9%
Kagi LLM Benchmark—55.2%
NYT Connections (extended)—16.8%
ARC-AGI-1—63.7%
Epoch Capabilities Index—146.68

Math Not comparable

GLM-4.6V: —, MiniMax-M2.5: 25.8 (#242)

BenchmarkGLM-4.6VMiniMax-M2.5
ProofBench—4%
LMArena Math—1378

Knowledge MiniMax-M2.5 leads

GLM-4.6V: 37.4 (#148), MiniMax-M2.5: 38.6 (#132)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Expert13721378
Vectara Hallucination Rate—9.1%

Multimodal Not comparable

GLM-4.6V: 34.2 (#83), MiniMax-M2.5: —

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Vision1164—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.8 (#135), MiniMax-M2.5: 47.3 (#147)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Non-English13591338
LMArena Chinese14261392
LMArena Russian13411359
LMArena French—1363
LMArena German—1361
LMArena Japanese—1169
LMArena Korean—1232
LMArena Spanish—1354

Instruction Following Too close to call

GLM-4.6V: 71.6 (#145), MiniMax-M2.5: 71.7 (#143)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Instruction Following13511353

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#138), MiniMax-M2.5: 37.4 (#211)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Longer Query13581367
CL-bench—11.4%
CL-bench Life—6.3%

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.8 (#132), MiniMax-M2.5: 54.1 (#149)

BenchmarkGLM-4.6VMiniMax-M2.5
LMArena Text13771359
LMArena Creative Writing13471331
LMArena Multi-Turn13591364
EQ-Bench Creative Writing—1361

Frequently asked questions

Is GLM-4.6V better than MiniMax-M2.5?

GLM-4.6V is the stronger model overall, scoring 40.9 to 38.1 on the Noometry Index.

Which is cheaper, GLM-4.6V or MiniMax-M2.5?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; MiniMax-M2.5 lists at $0.30 and $1.20.

Is GLM-4.6V or MiniMax-M2.5 better for coding?

MiniMax-M2.5 scores higher on coding benchmarks: 47.9 versus 40.2 in the Noometry coding category.

Which has the bigger context window?

MiniMax-M2.5 does, with 205K tokens against 128K.

How many benchmarks do GLM-4.6V and MiniMax-M2.5 share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and MiniMax-M2.5 has 33.

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