Noometry

Model comparison

GLM-4.6 vs Mixtral 8x22B

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 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and Mixtral 8x22B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.6 leads 40.2 to 15.1.
  • GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-4.6 accepts more context: 205K tokens versus 64K.

Side by side

GLM-4.6Mixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index41.427.1
Released2025-09-302024-04-17
WeightsOpenOpen
Context window205K64K
Max output131K64K
Input $ / M tokens$0.60$2
Output $ / M tokens$2.20$6
Results tracked2934

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Mixtral 8x22B: 24.2 (#329)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Coding14491166
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
WeirdML—3.2%
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
ALE-Bench340.82—
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use GLM-4.6 leads

GLM-4.6: 32.3 (#66), Mixtral 8x22B: 23.1 (#127)

BenchmarkGLM-4.6Mixtral 8x22B
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—
Cybench—7.5%

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Mixtral 8x22B: 19.9 (#248)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Hard Prompts14401150
Kagi LLM Benchmark47.4%—
CritPt1.1%—
DTBench—55.1%
Epoch Capabilities Index—122.03
ForecastBench—56.3

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Mixtral 8x22B: 22.9 (#275)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Math14321184
Omni-MATH—16.3%
MATH Level 5—24.2%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Mixtral 8x22B: 15.1 (#293)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Expert14311113
GPQA Diamond—34.1%
MMLU-Pro—46%
Vectara Hallucination Rate9.5%—
GPQA (HELM)—33.4%
MMLU—77.8%

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Mixtral 8x22B: 32.8 (#255)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Non-English14261128
LMArena Chinese14991116
LMArena French14591166
LMArena German14471141
LMArena Japanese13931037
LMArena Korean14001057
LMArena Russian14191158
LMArena Spanish14361151

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Mixtral 8x22B: 57.7 (#266)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Instruction Following14101147
IFEval—72.4%

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), Mixtral 8x22B: 34.7 (#247)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Longer Query14221144

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Mixtral 8x22B: 36.9 (#262)

BenchmarkGLM-4.6Mixtral 8x22B
LMArena Text14401162
LMArena Creative Writing14111141
LMArena Multi-Turn14271130
EQ-Bench Creative Writing1411—
WildBench—71.1%

Frequently asked questions

Is GLM-4.6 better than Mixtral 8x22B?

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 8x22B?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Mixtral 8x22B lists at $2 and $6.

Is GLM-4.6 or Mixtral 8x22B better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 64K.

How many benchmarks do GLM-4.6 and Mixtral 8x22B share?

17 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Mixtral 8x22B has 34.

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