Noometry

Model comparison

GLM-4.6 vs Mixtral 8x7B

GLM-4.6 is the stronger model overall, scoring 41.4 to 27.1 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.6 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-4.6 leads 40.2 to 11.0.
  • Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • GLM-4.6 accepts more context: 205K tokens versus 32K.

Side by side

GLM-4.6Mixtral 8x7B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index41.427.1
Released2025-09-302023-12-11
WeightsOpenOpen
Context window205K32K
Max output131K32K
Input $ / M tokens$0.60$0.70
Output $ / M tokens$2.20$0.70
Results tracked2938

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Mixtral 8x7B: 32.8 (#269)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Coding14491126
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
ALE-Bench340.82—
HumanEval+—39.6%
MBPP+—49.7%

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Mixtral 8x7B: —

BenchmarkGLM-4.6Mixtral 8x7B
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Mixtral 8x7B: 18.2 (#285)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Hard Prompts14401115
Kagi LLM Benchmark47.4%—
CritPt1.1%—
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-4.6 leads

GLM-4.6: 39.1 (#111), Mixtral 8x7B: 18.8 (#289)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Math14321147
Omni-MATH—10.5%
MATH Level 5—10%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—
GSM8K—74.4%

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Mixtral 8x7B: 11.0 (#301)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Expert14311088
GPQA Diamond—30.6%
MMLU-Pro—33.5%
Vectara Hallucination Rate9.5%—
GPQA (HELM)—29.6%
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Mixtral 8x7B: 29.6 (#266)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Non-English14261077
LMArena Chinese14991055
LMArena French14591166
LMArena German14471114
LMArena Japanese1393931
LMArena Korean1400968
LMArena Russian14191090
LMArena Spanish14361111

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Mixtral 8x7B: 51.0 (#297)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Instruction Following14101109
IFEval—57.5%

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), Mixtral 8x7B: 33.4 (#260)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Longer Query14221103

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Mixtral 8x7B: 34.2 (#270)

BenchmarkGLM-4.6Mixtral 8x7B
LMArena Text14401132
LMArena Creative Writing14111109
LMArena Multi-Turn14271115
EQ-Bench Creative Writing1411—
WildBench—67.3%

Frequently asked questions

Is GLM-4.6 better than Mixtral 8x7B?

GLM-4.6 is the stronger model overall, scoring 41.4 to 27.1 on the Noometry Index.

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

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

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

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 32K.

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

17 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Mixtral 8x7B has 38.

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