Model comparison

GLM-4.6V vs MiniMax-M2.7

GLM-4.6V is the stronger model overall, scoring 41.3 to 37.7 on the Noometry Index.

Last verified October 10, 2026. 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

41.3

Rank #137 Confirmed

MiniMax-M2.7

MiniMax

37.7

Rank #196 Confirmed

Summary

They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 2 categories and MiniMax-M2.7 in 5 categories; 5 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 19.7.
GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.7.
MiniMax-M2.7 accepts more context: 205K tokens versus 128K.

Side by side

GLM-4.6V and MiniMax-M2.7 specifications
	GLM-4.6V	MiniMax-M2.7
Provider	Z.ai (Zhipu)	MiniMax
Noometry Index	41.3	37.7
Released	2025-12-08	2026-03-18
Weights	Open	Open
Context window	128K	205K
Max output	33K	131K
Input $ / M tokens	$0.30	$0.30
Output $ / M tokens	$0.90	$1.20
Results tracked	12	30

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Category by category

Coding Too close to call

GLM-4.6V: 40.9 (#128), MiniMax-M2.7: 41.8 (#120)

Coding benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Coding	1390	1454
LMArena WebDev	—	1398
SciCode	—	47%
WeirdML	—	37%
ALE-Bench	—	599.25

Agentic & Tool Use Not comparable

GLM-4.6V: —, MiniMax-M2.7: 25.1 (#111)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
Terminal-Bench	—	45.1%
ExploitBench	—	13.3%
GBAEval	—	0%

Reasoning GLM-4.6V leads

GLM-4.6V: 27.6 (#115), MiniMax-M2.7: 19.7 (#253)

Reasoning benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Hard Prompts	1368	1422
NYT Connections (extended)	—	24.7%
CritPt	—	0.6%
Thematic Generalization	—	39.3%
Epoch Capabilities Index	—	145.85

Math Not comparable

GLM-4.6V: —, MiniMax-M2.7: 25.9 (#263)

Math benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
ProofBench	—	3%
LMArena Math	—	1420

Knowledge Too close to call

GLM-4.6V: 38.0 (#149), MiniMax-M2.7: 37.7 (#152)

Knowledge benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Expert	1371	1444
Vectara Hallucination Rate	—	12.9%

Multimodal Not comparable

GLM-4.6V: 34.8 (#90), MiniMax-M2.7: —

Multimodal benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Vision	1164	—

Multilingual MiniMax-M2.7 leads

GLM-4.6V: 48.6 (#141), MiniMax-M2.7: 50.3 (#123)

Multilingual benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Non-English	1359	1382
LMArena Chinese	1425	1441
LMArena Russian	1340	1383
LMArena French	—	1421
LMArena German	—	1398
LMArena Japanese	—	1262
LMArena Korean	—	1313
LMArena Spanish	—	1403

Instruction Following MiniMax-M2.7 leads

GLM-4.6V: 71.4 (#151), MiniMax-M2.7: 74.1 (#103)

Instruction Following benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Instruction Following	1352	1405

Long Context MiniMax-M2.7 leads

GLM-4.6V: 41.3 (#143), MiniMax-M2.7: 43.3 (#99)

Long Context benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Longer Query	1358	1419

Writing & Preference MiniMax-M2.7 leads

GLM-4.6V: 56.6 (#137), MiniMax-M2.7: 58.9 (#112)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	MiniMax-M2.7
LMArena Text	1377	1405
LMArena Creative Writing	1347	1354
LMArena Multi-Turn	1360	1412

Frequently asked questions

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

GLM-4.6V is the stronger model overall, scoring 41.3 to 37.7 on the Noometry Index.

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

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; MiniMax-M2.7 lists at $0.30 and $1.20.

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

They score almost the same on coding (40.9 vs 41.8); test both on your own repository before choosing.

Which has the bigger context window?

MiniMax-M2.7 does, with 205K tokens against 128K.

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

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and MiniMax-M2.7 has 30.