Noometry

Model comparison

GLM-4.5V vs Mixtral 8x7B

GLM-4.5V is the stronger model overall, scoring 39.8 to 27.1 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.5V leads 37.5 to 11.0.
  • Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • GLM-4.5V accepts more context: 64K tokens versus 32K.

Side by side

GLM-4.5VMixtral 8x7B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index39.827.1
Released2025-08-112023-12-11
WeightsOpenOpen
Context window64K32K
Max output16K32K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$1.80$0.70
Results tracked1538

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Mixtral 8x7B: 32.8 (#269)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Coding13471126
HumanEval+—39.6%
MBPP+—49.7%

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), Mixtral 8x7B: 18.2 (#285)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Hard Prompts13341115
Kagi LLM Benchmark59.8%—
DTBench—49.6%
Adversarial NLI—55.2%
Epoch Capabilities Index—118.47
ForecastBench—56.3
HellaSwag—86.7%
PIQA—83.6%
WinoGrande—77.2%

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), Mixtral 8x7B: 18.8 (#289)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Math13541147
Omni-MATH—10.5%
MATH Level 5—10%
GSM8K—74.4%

Knowledge GLM-4.5V leads

GLM-4.5V: 37.5 (#156), Mixtral 8x7B: 11.0 (#301)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Expert13531088
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-4.5V: 34.3 (#92), Mixtral 8x7B: —

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Vision1154—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.6 (#177), Mixtral 8x7B: 29.6 (#266)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Non-English13031077
LMArena Chinese13371055
LMArena Russian12981090
LMArena Spanish13361111
LMArena French—1166
LMArena German—1114
LMArena Japanese—931
LMArena Korean—968

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.2 (#175), Mixtral 8x7B: 51.0 (#297)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Instruction Following13111109
IFEval—57.5%

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#171), Mixtral 8x7B: 33.4 (#260)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Longer Query13041103

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.5 (#170), Mixtral 8x7B: 34.2 (#270)

BenchmarkGLM-4.5VMixtral 8x7B
LMArena Text13331132
LMArena Creative Writing12951109
LMArena Multi-Turn13321115
WildBench—67.3%

Frequently asked questions

Is GLM-4.5V better than Mixtral 8x7B?

GLM-4.5V is the stronger model overall, scoring 39.8 to 27.1 on the Noometry Index.

Which is cheaper, GLM-4.5V or Mixtral 8x7B?

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Mixtral 8x7B better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5V does, with 64K tokens against 32K.

How many benchmarks do GLM-4.5V and Mixtral 8x7B share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Mixtral 8x7B has 38.

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