Model comparison

GLM-4.6V vs MiniMax-M2

GLM-4.6V is the stronger model overall, scoring 41.3 to 37.4 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

MiniMax

37.4

Rank #204 Confirmed

Summary

They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and MiniMax-M2 in 0 categories; 6 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 19.4.
GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.30 / $1.20 for MiniMax-M2.
MiniMax-M2 accepts more context: 205K tokens versus 128K.

Side by side

GLM-4.6V and MiniMax-M2 specifications
	GLM-4.6V	MiniMax-M2
Provider	Z.ai (Zhipu)	MiniMax
Noometry Index	41.3	37.4
Released	2025-12-08	2025-10-27
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	21

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.9 (#128), MiniMax-M2: 39.3 (#159)

Coding benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Coding	1390	1370
SWE-bench Verified (bash only)	—	61%
LMArena WebDev	—	1297

Agentic & Tool Use Not comparable

GLM-4.6V: —, MiniMax-M2: 25.1 (#109)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
Terminal-Bench	—	30%
Vending-Bench 2	—	160.6

Reasoning GLM-4.6V leads

GLM-4.6V: 27.6 (#115), MiniMax-M2: 19.4 (#258)

Reasoning benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Hard Prompts	1368	1357
Kagi LLM Benchmark	—	57.8%
NYT Connections (extended)	—	14.8%

Math Not comparable

GLM-4.6V: —, MiniMax-M2: 37.3 (#160)

Math benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Math	—	1352

Knowledge GLM-4.6V leads

GLM-4.6V: 38.0 (#149), MiniMax-M2: 37.0 (#163)

Knowledge benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Expert	1371	1337

Multimodal Not comparable

GLM-4.6V: 34.8 (#90), MiniMax-M2: —

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

Multilingual GLM-4.6V leads

GLM-4.6V: 48.6 (#141), MiniMax-M2: 45.3 (#171)

Multilingual benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Non-English	1359	1313
LMArena Chinese	1425	1366
LMArena Russian	1340	1331
LMArena French	—	1335
LMArena German	—	1355
LMArena Spanish	—	1326

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.4 (#151), MiniMax-M2: 70.2 (#166)

Instruction Following benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Instruction Following	1352	1328

Long Context Too close to call

GLM-4.6V: 41.3 (#143), MiniMax-M2: 40.5 (#153)

Long Context benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Longer Query	1358	1331

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.6 (#137), MiniMax-M2: 53.0 (#162)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	MiniMax-M2
LMArena Text	1377	1340
LMArena Creative Writing	1347	1286
LMArena Multi-Turn	1360	1361

Frequently asked questions

Is GLM-4.6V better than MiniMax-M2?

GLM-4.6V is the stronger model overall, scoring 41.3 to 37.4 on the Noometry Index.

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

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

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

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 39.3 in the Noometry coding category.

Which has the bigger context window?

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

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

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and MiniMax-M2 has 21.