Noometry

Model comparison

GLM-4.7-Flash vs MiniMax-M2

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 36.9 on the Noometry Index.

Last verified . 15 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.4

Rank #175 Confirmed

MiniMax-M2 MiniMax

36.9

Rank #201 Confirmed

Summary

  • They share 15 benchmarks with published results for both. GLM-4.7-Flash scores higher in 4 categories and MiniMax-M2 in 4 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where MiniMax-M2 leads 53.3 to 47.7.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.
  • MiniMax-M2 accepts more context: 205K tokens versus 200K.

Side by side

GLM-4.7-FlashMiniMax-M2
ProviderZ.ai (Zhipu)MiniMax
Noometry Index38.436.9
Released2026-01-192025-10-27
WeightsOpenOpen
Context window200K205K
Max output131K131K
Input $ / M tokens$0.06$0.30
Output $ / M tokens$0.40$1.20
Results tracked2121

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Category by category

Coding Too close to call

GLM-4.7-Flash: 40.0 (#133), MiniMax-M2: 39.1 (#151)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Coding13831370
SWE-bench Verified (bash only)—61%
LMArena WebDev—1297

Agentic & Tool Use Not comparable

GLM-4.7-Flash: —, MiniMax-M2: 24.9 (#102)

BenchmarkGLM-4.7-FlashMiniMax-M2
Terminal-Bench—30%
Vending-Bench 2—160.6

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.2 (#224), MiniMax-M2: 18.6 (#256)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Hard Prompts13561357
Kagi LLM Benchmark—57.8%
NYT Connections (extended)—14.8%
Chess Puzzles0%—

Math Too close to call

GLM-4.7-Flash: 34.4 (#159), MiniMax-M2: 34.6 (#158)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Math13541350
OTIS Mock AIME 2024-202558.3%—

Knowledge Too close to call

GLM-4.7-Flash: 35.4 (#177), MiniMax-M2: 36.3 (#160)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Expert13571336
GPQA Diamond60.5%—
Vectara Hallucination Rate9.3%—

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.8 (#152), MiniMax-M2: 45.6 (#165)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Non-English13311314
LMArena Chinese14031367
LMArena French13321336
LMArena German13361366
LMArena Russian13331332
LMArena Spanish13511326
LMArena Korean1284—

Instruction Following Too close to call

GLM-4.7-Flash: 70.3 (#162), MiniMax-M2: 70.4 (#160)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Instruction Following13271329

Long Context Too close to call

GLM-4.7-Flash: 40.9 (#143), MiniMax-M2: 40.5 (#148)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Longer Query13451332

Writing & Preference MiniMax-M2 leads

GLM-4.7-Flash: 47.7 (#206), MiniMax-M2: 53.3 (#157)

BenchmarkGLM-4.7-FlashMiniMax-M2
LMArena Text13511340
LMArena Creative Writing12971288
LMArena Multi-Turn13421362
EQ-Bench Creative Writing1125—

Frequently asked questions

Is GLM-4.7-Flash better than MiniMax-M2?

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 36.9 on the Noometry Index.

Which is cheaper, GLM-4.7-Flash or MiniMax-M2?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; MiniMax-M2 lists at $0.30 and $1.20.

Is GLM-4.7-Flash or MiniMax-M2 better for coding?

They score almost the same on coding (40.0 vs 39.1); test both on your own repository before choosing.

Which has the bigger context window?

MiniMax-M2 does, with 205K tokens against 200K.

How many benchmarks do GLM-4.7-Flash and MiniMax-M2 share?

15 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and MiniMax-M2 has 21.

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