Noometry

Model comparison

GLM-4.6V vs Mixtral 8x7B

GLM-4.6V is the stronger model overall, scoring 40.9 to 26.8 on the Noometry Index.

Last verified . 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Mixtral 8x7B Mistral AI

26.8

Rank #328 Confirmed

Summary

  • They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Mixtral 8x7B in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 11.0.
  • GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
  • GLM-4.6V accepts more context: 128K tokens versus 32K.

Side by side

GLM-4.6VMixtral 8x7B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index40.926.8
Released2025-12-082023-12-11
WeightsOpenOpen
Context window128K32K
Max output33K32K
Input $ / M tokens$0.30$0.70
Output $ / M tokens$0.90$0.70
Results tracked1238

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Mixtral 8x7B: 32.2 (#265)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Coding13901127
HumanEval+—39.6%
MBPP+—49.7%

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), Mixtral 8x7B: 17.4 (#283)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Hard Prompts13681115
DTBench—49.6%
Adversarial NLI—55.2%
Epoch Capabilities Index—118.38
ForecastBench—56.3
HellaSwag—86.7%
PIQA—83.6%
WinoGrande—77.2%

Math Not comparable

GLM-4.6V: —, Mixtral 8x7B: 17.2 (#284)

BenchmarkGLM-4.6VMixtral 8x7B
Omni-MATH—10.5%
LMArena Math—1147
MATH Level 5—10%
GSM8K—74.4%

Knowledge GLM-4.6V leads

GLM-4.6V: 37.4 (#148), Mixtral 8x7B: 11.0 (#296)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Expert13721088
GPQA Diamond—30.6%
MMLU-Pro—33.5%
GPQA (HELM)—29.6%
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multimodal Not comparable

GLM-4.6V: 34.2 (#83), Mixtral 8x7B: —

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Vision1164—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.8 (#135), Mixtral 8x7B: 29.8 (#261)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Non-English13591077
LMArena Chinese14261055
LMArena Russian13411090
LMArena French—1165
LMArena German—1114
LMArena Japanese—931
LMArena Korean—968
LMArena Spanish—1110

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.6 (#145), Mixtral 8x7B: 51.2 (#292)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Instruction Following13511110
IFEval—57.5%

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#138), Mixtral 8x7B: 33.4 (#255)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Longer Query13581103

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.8 (#132), Mixtral 8x7B: 34.4 (#265)

BenchmarkGLM-4.6VMixtral 8x7B
LMArena Text13771132
LMArena Creative Writing13471109
LMArena Multi-Turn13591115
WildBench—67.3%

Frequently asked questions

Is GLM-4.6V better than Mixtral 8x7B?

GLM-4.6V is the stronger model overall, scoring 40.9 to 26.8 on the Noometry Index.

Which is cheaper, GLM-4.6V or Mixtral 8x7B?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

Is GLM-4.6V or Mixtral 8x7B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 32.2 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6V does, with 128K tokens against 32K.

How many benchmarks do GLM-4.6V and Mixtral 8x7B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Mixtral 8x7B has 38.

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