Noometry

Model comparison

GLM-4.6V vs Mixtral 8x22B

GLM-4.6V is the stronger model overall, scoring 40.9 to 26.7 on the Noometry Index.

Last verified . 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Mixtral 8x22B Mistral AI

26.7

Rank #330 Confirmed

Summary

  • They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Mixtral 8x22B in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 15.1.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-4.6V accepts more context: 128K tokens versus 64K.

Side by side

GLM-4.6VMixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index40.926.7
Released2025-12-082024-04-17
WeightsOpenOpen
Context window128K64K
Max output33K64K
Input $ / M tokens$0.30$2
Output $ / M tokens$0.90$6
Results tracked1234

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Mixtral 8x22B: 23.8 (#324)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Coding13901166
WeirdML—3.2%
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use Not comparable

GLM-4.6V: —, Mixtral 8x22B: 22.8 (#119)

BenchmarkGLM-4.6VMixtral 8x22B
Cybench—7.5%

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), Mixtral 8x22B: 19.1 (#246)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Hard Prompts13681150
DTBench—55.1%
Epoch Capabilities Index—122
ForecastBench—56.3

Math Not comparable

GLM-4.6V: —, Mixtral 8x22B: 21.2 (#271)

BenchmarkGLM-4.6VMixtral 8x22B
Omni-MATH—16.3%
LMArena Math—1184
MATH Level 5—24.2%

Knowledge GLM-4.6V leads

GLM-4.6V: 37.4 (#148), Mixtral 8x22B: 15.1 (#288)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Expert13721113
GPQA Diamond—34.1%
MMLU-Pro—46%
GPQA (HELM)—33.4%
MMLU—77.8%

Multimodal Not comparable

GLM-4.6V: 34.2 (#83), Mixtral 8x22B: —

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Vision1164—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.8 (#135), Mixtral 8x22B: 32.9 (#250)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Non-English13591128
LMArena Chinese14261116
LMArena Russian13411158
LMArena French—1165
LMArena German—1141
LMArena Japanese—1037
LMArena Korean—1056
LMArena Spanish—1150

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.6 (#145), Mixtral 8x22B: 57.9 (#261)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Instruction Following13511147
IFEval—72.4%

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#138), Mixtral 8x22B: 34.6 (#242)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Longer Query13581144

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.8 (#132), Mixtral 8x22B: 37.1 (#257)

BenchmarkGLM-4.6VMixtral 8x22B
LMArena Text13771162
LMArena Creative Writing13471141
LMArena Multi-Turn13591130
WildBench—71.1%

Frequently asked questions

Is GLM-4.6V better than Mixtral 8x22B?

GLM-4.6V is the stronger model overall, scoring 40.9 to 26.7 on the Noometry Index.

Which is cheaper, GLM-4.6V or Mixtral 8x22B?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is GLM-4.6V or Mixtral 8x22B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 23.8 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6V does, with 128K tokens against 64K.

How many benchmarks do GLM-4.6V and Mixtral 8x22B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Mixtral 8x22B has 34.

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