Noometry

Model comparison

GLM-4.7 vs Llama 2-70B

GLM-4.7 is the stronger model overall, scoring 42.0 to 24.4 on the Noometry Index.

Last verified . 20 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Llama 2-70B Meta

24.4

Rank #349 Confirmed

Summary

  • They share 20 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and Llama 2-70B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 7.4.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 0% for Llama 2-70B.

Side by side

GLM-4.7Llama 2-70B
ProviderZ.ai (Zhipu)Meta
Noometry Index42.024.4
Released2025-12-222023-07-18
WeightsOpenOpen
Context window205K—
Max output131K—
Input $ / M tokens$0.60—
Output $ / M tokens$2.20—
Results tracked3635

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Llama 2-70B: 31.4 (#286)

BenchmarkGLM-4.7Llama 2-70B
LMArena Coding14541079
LMArena WebDev1435—
SciCode45.1%—
ALE-Bench399.48—

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), Llama 2-70B: —

BenchmarkGLM-4.7Llama 2-70B
Terminal-Bench33.4%—
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Llama 2-70B: 14.4 (#325)

BenchmarkGLM-4.7Llama 2-70B
LMArena Hard Prompts14431073
Epoch Capabilities Index143.51113.79
SimpleBench47.7%—
CritPt1.7%—
Chess Puzzles6%—
DTBench—41.6%
BIG-Bench Hard—64.9%
CommonsenseQA 2.0—50%
ForecastBench—51.4
HellaSwag—85.3%
LAMBADA—78.9%
PIQA—82.8%
WinoGrande—80.2%

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Llama 2-70B: 8.1 (#326)

BenchmarkGLM-4.7Llama 2-70B
OTIS Mock AIME 2024-202583.3%0%
LMArena Math14231091
ProofBench6%—
MATH Level 5—3.3%
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—
GSM8K—69.6%

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Llama 2-70B: 7.4 (#310)

BenchmarkGLM-4.7Llama 2-70B
GPQA Diamond83.3%26.3%
LMArena Expert14241039
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—
ARC (AI2) Challenge—78.3%
BoolQ—88.6%
MMLU—69.9%
OpenBookQA—60.2%
TriviaQA—87.6%

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), Llama 2-70B: 27.7 (#274)

BenchmarkGLM-4.7Llama 2-70B
LMArena Non-English14171045
LMArena Chinese1495995
LMArena French14321090
LMArena German14241041
LMArena Japanese1439927
LMArena Korean1399964
LMArena Russian14231083
LMArena Spanish14341143

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), Llama 2-70B: 54.9 (#278)

BenchmarkGLM-4.7Llama 2-70B
LMArena Instruction Following14111071

Long Context GLM-4.7 leads

GLM-4.7: 42.8 (#116), Llama 2-70B: 32.3 (#270)

BenchmarkGLM-4.7Llama 2-70B
LMArena Longer Query14321062
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), Llama 2-70B: 32.3 (#279)

BenchmarkGLM-4.7Llama 2-70B
LMArena Text14351115
LMArena Creative Writing14011075
LMArena Multi-Turn14461088
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than Llama 2-70B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 24.4 on the Noometry Index.

Is GLM-4.7 or Llama 2-70B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 31.4 in the Noometry coding category.

How many benchmarks do GLM-4.7 and Llama 2-70B share?

20 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 2-70B has 35.

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