Noometry

Model comparison

GLM-4.5V vs Mixtral 8x22B

GLM-4.5V is the stronger model overall, scoring 39.2 to 26.7 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.2

Rank #158 Confirmed

Mixtral 8x22B Mistral AI

26.7

Rank #330 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Mixtral 8x22B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.5V leads 36.8 to 15.1.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.

Side by side

GLM-4.5VMixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index39.226.7
Released2025-08-112024-04-17
WeightsOpenOpen
Context window64K64K
Max output16K64K
Input $ / M tokens$0.60$2
Output $ / M tokens$1.80$6
Results tracked1534

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 38.9 (#152), Mixtral 8x22B: 23.8 (#324)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Coding13471166
WeirdML—3.2%
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Mixtral 8x22B: 22.8 (#119)

BenchmarkGLM-4.5VMixtral 8x22B
Cybench—7.5%

Reasoning GLM-4.5V leads

GLM-4.5V: 26.5 (#118), Mixtral 8x22B: 19.1 (#246)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Hard Prompts13351150
Kagi LLM Benchmark59.8%—
DTBench—55.1%
Epoch Capabilities Index—122
ForecastBench—56.3

Math GLM-4.5V leads

GLM-4.5V: 34.7 (#157), Mixtral 8x22B: 21.2 (#271)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Math13531184
Omni-MATH—16.3%
MATH Level 5—24.2%

Knowledge GLM-4.5V leads

GLM-4.5V: 36.8 (#155), Mixtral 8x22B: 15.1 (#288)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Expert13531113
GPQA Diamond—34.1%
MMLU-Pro—46%
GPQA (HELM)—33.4%
MMLU—77.8%

Multimodal Not comparable

GLM-4.5V: 33.6 (#86), Mixtral 8x22B: —

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Vision1154—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.9 (#172), Mixtral 8x22B: 32.9 (#250)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Non-English13041128
LMArena Chinese13381116
LMArena Russian12991158
LMArena Spanish13351150
LMArena French—1165
LMArena German—1141
LMArena Japanese—1037
LMArena Korean—1056

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.4 (#170), Mixtral 8x22B: 57.9 (#261)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Instruction Following13111147
IFEval—72.4%

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#166), Mixtral 8x22B: 34.6 (#242)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Longer Query13051144

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.7 (#162), Mixtral 8x22B: 37.1 (#257)

BenchmarkGLM-4.5VMixtral 8x22B
LMArena Text13331162
LMArena Creative Writing12951141
LMArena Multi-Turn13331130
WildBench—71.1%

Frequently asked questions

Is GLM-4.5V better than Mixtral 8x22B?

GLM-4.5V is the stronger model overall, scoring 39.2 to 26.7 on the Noometry Index.

Which is cheaper, GLM-4.5V or Mixtral 8x22B?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is GLM-4.5V or Mixtral 8x22B better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 23.8 in the Noometry coding category.

Which has the bigger context window?

Both accept 64K tokens.

How many benchmarks do GLM-4.5V and Mixtral 8x22B share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Mixtral 8x22B has 34.

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