Noometry

Model comparison

GLM-4.7 vs Llama 3-8B

GLM-4.7 is the stronger model overall, scoring 42.0 to 25.5 on the Noometry Index.

Last verified . 21 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Llama 3-8B Meta

25.5

Rank #344 Confirmed

Summary

  • They share 21 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 7.8.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 1.9% for Llama 3-8B.

Side by side

GLM-4.7Llama 3-8B
ProviderZ.ai (Zhipu)Meta
Noometry Index42.025.5
Released2025-12-222024-04-18
WeightsOpenOpen
Context window205K—
Max output131K—
Input $ / M tokens$0.60—
Output $ / M tokens$2.20—
Results tracked3634

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Llama 3-8B: 31.0 (#289)

BenchmarkGLM-4.7Llama 3-8B
LMArena Coding14541152
LMArena WebDev1435—
SciCode45.1%—
BigCodeBench Instruct—31.9%
BigCodeBench Complete—36.9%
ALE-Bench399.48—
HumanEval+—56.7%
MBPP+—54.8%

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), Llama 3-8B: —

BenchmarkGLM-4.7Llama 3-8B
Terminal-Bench33.4%—
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Llama 3-8B: 14.3 (#326)

BenchmarkGLM-4.7Llama 3-8B
Chess Puzzles6%0%
LMArena Hard Prompts14431133
Epoch Capabilities Index143.51116.45
SimpleBench47.7%—
CritPt1.7%—
DTBench—43.9%
Adversarial NLI—57.3%
ForecastBench—58.6
WinoGrande—75.7%

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Llama 3-8B: 8.8 (#323)

BenchmarkGLM-4.7Llama 3-8B
OTIS Mock AIME 2024-202583.3%1.9%
LMArena Math14231151
ProofBench6%—
MATH Level 5—6.1%
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Llama 3-8B: 7.8 (#308)

BenchmarkGLM-4.7Llama 3-8B
GPQA Diamond83.3%26.1%
LMArena Expert14241113
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—
ARC (AI2) Challenge—82.8%
MMLU—68.8%
OpenBookQA—82.6%
TriviaQA—67.7%

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), Llama 3-8B: 30.8 (#261)

BenchmarkGLM-4.7Llama 3-8B
LMArena Non-English14171098
LMArena Chinese14951076
LMArena French14321159
LMArena German14241104
LMArena Japanese1439967
LMArena Korean13991004
LMArena Russian14231109
LMArena Spanish14341173

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), Llama 3-8B: 58.4 (#260)

BenchmarkGLM-4.7Llama 3-8B
LMArena Instruction Following14111127

Long Context GLM-4.7 leads

GLM-4.7: 42.8 (#116), Llama 3-8B: 34.2 (#251)

BenchmarkGLM-4.7Llama 3-8B
LMArena Longer Query14321128
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), Llama 3-8B: 37.5 (#256)

BenchmarkGLM-4.7Llama 3-8B
LMArena Text14351166
LMArena Creative Writing14011150
LMArena Multi-Turn14461152
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than Llama 3-8B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 25.5 on the Noometry Index.

Is GLM-4.7 or Llama 3-8B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 31.0 in the Noometry coding category.

How many benchmarks do GLM-4.7 and Llama 3-8B share?

21 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 3-8B has 34.

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