Noometry

Model comparison

GLM-4.5V vs MiniMax-M2.7

GLM-4.5V is the stronger model overall, scoring 39.8 to 37.7 on the Noometry Index. MiniMax-M2.7 costs 1.7× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

MiniMax-M2.7 MiniMax

37.7

Rank #196 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 2 categories and MiniMax-M2.7 in 6 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.5V leads 37.4 to 25.9.
  • MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • MiniMax-M2.7 accepts more context: 205K tokens versus 64K.

Side by side

GLM-4.5VMiniMax-M2.7
ProviderZ.ai (Zhipu)MiniMax
Noometry Index39.837.7
Released2025-08-112026-03-18
WeightsOpenOpen
Context window64K205K
Max output16K131K
Input $ / M tokens$0.60$0.30
Output $ / M tokens$1.80$1.20
Results tracked1530

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Category by category

Coding MiniMax-M2.7 leads

GLM-4.5V: 39.5 (#155), MiniMax-M2.7: 41.8 (#120)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Coding13471454
LMArena WebDev—1398
SciCode—47%
WeirdML—37%
ALE-Bench—599.25

Agentic & Tool Use Not comparable

GLM-4.5V: —, MiniMax-M2.7: 25.1 (#111)

BenchmarkGLM-4.5VMiniMax-M2.7
Terminal-Bench—45.1%
ExploitBench—13.3%
GBAEval—0%

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), MiniMax-M2.7: 19.7 (#253)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Hard Prompts13341422
Kagi LLM Benchmark59.8%—
NYT Connections (extended)—24.7%
CritPt—0.6%
Thematic Generalization—39.3%
Epoch Capabilities Index—145.85

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), MiniMax-M2.7: 25.9 (#263)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Math13541420
ProofBench—3%

Knowledge Too close to call

GLM-4.5V: 37.5 (#156), MiniMax-M2.7: 37.7 (#152)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Expert13531444
Vectara Hallucination Rate—12.9%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), MiniMax-M2.7: —

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Vision1154—

Multilingual MiniMax-M2.7 leads

GLM-4.5V: 44.6 (#177), MiniMax-M2.7: 50.3 (#123)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Non-English13031382
LMArena Chinese13371441
LMArena Russian12981383
LMArena Spanish13361403
LMArena French—1421
LMArena German—1398
LMArena Japanese—1262
LMArena Korean—1313

Instruction Following MiniMax-M2.7 leads

GLM-4.5V: 69.2 (#175), MiniMax-M2.7: 74.1 (#103)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Instruction Following13111405

Long Context MiniMax-M2.7 leads

GLM-4.5V: 39.6 (#171), MiniMax-M2.7: 43.3 (#99)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Longer Query13041419

Writing & Preference MiniMax-M2.7 leads

GLM-4.5V: 52.5 (#170), MiniMax-M2.7: 58.9 (#112)

BenchmarkGLM-4.5VMiniMax-M2.7
LMArena Text13331405
LMArena Creative Writing12951354
LMArena Multi-Turn13321412

Frequently asked questions

Is GLM-4.5V better than MiniMax-M2.7?

GLM-4.5V is the stronger model overall, scoring 39.8 to 37.7 on the Noometry Index. MiniMax-M2.7 costs 1.7× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or MiniMax-M2.7 better for coding?

MiniMax-M2.7 scores higher on coding benchmarks: 41.8 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

MiniMax-M2.7 does, with 205K tokens against 64K.

How many benchmarks do GLM-4.5V and MiniMax-M2.7 share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and MiniMax-M2.7 has 30.

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