Noometry

Model comparison

GLM-4.7 vs Mixtral 8x22B

GLM-4.7 is the stronger model overall, scoring 42.0 to 27.1 on the Noometry Index.

Last verified . 19 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 19 benchmarks with published results for both. GLM-4.7 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-4.7 leads 47.0 to 15.1.
  • The biggest single-benchmark swing is GPQA Diamond: 83.3% for GLM-4.7 and 34.1% for Mixtral 8x22B.
  • GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Mixtral 8x22B.
  • GLM-4.7 accepts more context: 205K tokens versus 64K.

Side by side

GLM-4.7Mixtral 8x22B
ProviderZ.ai (Zhipu)Mistral AI
Noometry Index42.027.1
Released2025-12-222024-04-17
WeightsOpenOpen
Context window205K64K
Max output131K64K
Input $ / M tokens$0.60$2
Output $ / M tokens$2.20$6
Results tracked3634

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Mixtral 8x22B: 24.2 (#329)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Coding14541166
LMArena WebDev1435—
SciCode45.1%—
WeirdML—3.2%
BigCodeBench Instruct—40.6%
BigCodeBench Complete—50.2%
ALE-Bench399.48—
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use GLM-4.7 leads

GLM-4.7: 26.5 (#103), Mixtral 8x22B: 23.1 (#127)

BenchmarkGLM-4.7Mixtral 8x22B
Terminal-Bench33.4%—
Cybench—7.5%
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Mixtral 8x22B: 19.9 (#248)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Hard Prompts14431150
Epoch Capabilities Index143.51122.03
SimpleBench47.7%—
CritPt1.7%—
Chess Puzzles6%—
DTBench—55.1%
ForecastBench—56.3

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Mixtral 8x22B: 22.9 (#275)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Math14231184
OTIS Mock AIME 2024-202583.3%—
ProofBench6%—
Omni-MATH—16.3%
MATH Level 5—24.2%
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Mixtral 8x22B: 15.1 (#293)

BenchmarkGLM-4.7Mixtral 8x22B
GPQA Diamond83.3%34.1%
LMArena Expert14241113
SimpleQA Verified32.2%—
MMLU-Pro—46%
Vectara Hallucination Rate11.7%—
GPQA (HELM)—33.4%
MMLU—77.8%

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), Mixtral 8x22B: 32.8 (#255)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Non-English14171128
LMArena Chinese14951116
LMArena French14321166
LMArena German14241141
LMArena Japanese14391037
LMArena Korean13991057
LMArena Russian14231158
LMArena Spanish14341151

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), Mixtral 8x22B: 57.7 (#266)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Instruction Following14111147
IFEval—72.4%

Long Context GLM-4.7 leads

GLM-4.7: 42.8 (#116), Mixtral 8x22B: 34.7 (#247)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Longer Query14321144
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), Mixtral 8x22B: 36.9 (#262)

BenchmarkGLM-4.7Mixtral 8x22B
LMArena Text14351162
LMArena Creative Writing14011141
LMArena Multi-Turn14461130
EQ-Bench Creative Writing1413—
WildBench—71.1%

Frequently asked questions

Is GLM-4.7 better than Mixtral 8x22B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 27.1 on the Noometry Index.

Which is cheaper, GLM-4.7 or Mixtral 8x22B?

GLM-4.7 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-4.7 or Mixtral 8x22B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 64K.

How many benchmarks do GLM-4.7 and Mixtral 8x22B share?

19 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Mixtral 8x22B has 34.

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