Noometry

Model comparison

GLM-4.5 vs Mixtral 8x7B

GLM-4.5 is the stronger model overall, scoring 42.0 to 27.1 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.5 Z.ai (Zhipu)

42.0

Rank #122 Confirmed

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.5 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-4.5 leads 35.9 to 11.0.
  • Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
  • GLM-4.5 accepts more context: 131K tokens versus 32K.

Side by side

GLM-4.5Mixtral 8x7B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index42.027.1
Released2025-07-272023-12-11
WeightsOpenOpen
Context window131K32K
Max output98K32K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$2.20$0.70
Results tracked2738

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Category by category

Coding GLM-4.5 leads

GLM-4.5: 41.4 (#125), Mixtral 8x7B: 32.8 (#269)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Coding14341126
SWE-bench Verified (bash only)54.2%—
WeirdML40.6%—
ALE-Bench344.82—
AlgoTune1.52—
HumanEval+—39.6%
MBPP+—49.7%

Reasoning GLM-4.5 leads

GLM-4.5: 28.6 (#100), Mixtral 8x7B: 18.2 (#285)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Hard Prompts14291115
Kagi LLM Benchmark57.9%—
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-4.5 leads

GLM-4.5: 39.0 (#116), Mixtral 8x7B: 18.8 (#289)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Math14271147
Omni-MATH—10.5%
MATH Level 5—10%
GSM8K—74.4%

Knowledge GLM-4.5 leads

GLM-4.5: 35.9 (#179), Mixtral 8x7B: 11.0 (#301)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Expert14331088
GPQA Diamond—30.6%
Humanity's Last Exam8.3%—
MMLU-Pro—33.5%
Confabulations11.3%—
GPQA (HELM)—29.6%
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multilingual GLM-4.5 leads

GLM-4.5: 52.8 (#77), Mixtral 8x7B: 29.6 (#266)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Non-English14171077
LMArena Chinese14651055
LMArena French14181166
LMArena German14071114
LMArena Japanese1415931
LMArena Korean1380968
LMArena Russian14141090
LMArena Spanish14541111

Instruction Following GLM-4.5 leads

GLM-4.5: 74.1 (#104), Mixtral 8x7B: 51.0 (#297)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Instruction Following14041109
IFEval—57.5%

Long Context GLM-4.5 leads

GLM-4.5: 38.2 (#201), Mixtral 8x7B: 33.4 (#260)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Longer Query14121103
Fiction.LiveBench58.3%—

Writing & Preference GLM-4.5 leads

GLM-4.5: 57.5 (#127), Mixtral 8x7B: 34.2 (#270)

BenchmarkGLM-4.5Mixtral 8x7B
LMArena Text14301132
LMArena Creative Writing13951109
LMArena Multi-Turn14151115
Short-Story Creative Writing73.4%—
EQ-Bench Creative Writing1343—
WildBench—67.3%

Frequently asked questions

Is GLM-4.5 better than Mixtral 8x7B?

GLM-4.5 is the stronger model overall, scoring 42.0 to 27.1 on the Noometry Index.

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

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

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

GLM-4.5 scores higher on coding benchmarks: 41.4 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

GLM-4.5 does, with 131K tokens against 32K.

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

17 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Mixtral 8x7B has 38.

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