Noometry

Model comparison

GLM-4.7 vs Llama 3.2 3B

GLM-4.7 is the stronger model overall, scoring 42.0 to 28.9 on the Noometry Index. Llama 3.2 3B costs 8.3× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

Last verified . 14 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Llama 3.2 3B Meta

28.9

Rank #321 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Llama 3.2 3B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 24.7.
  • Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • GLM-4.7 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.7Llama 3.2 3B
ProviderZ.ai (Zhipu)Meta
Noometry Index42.028.9
Released2025-12-222024-09-24
WeightsOpenOpen
Context window205K131K
Max output131K118K
Input $ / M tokens$0.60$0.05
Output $ / M tokens$2.20$0.33
Results tracked3618

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Llama 3.2 3B: 27.6 (#319)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Coding14541098
LMArena WebDev1435—
SciCode45.1%—
BigCodeBench Instruct—23.4%
BigCodeBench Complete—28.3%
ALE-Bench399.48—

Agentic & Tool Use GLM-4.7 leads

GLM-4.7: 26.5 (#103), Llama 3.2 3B: 20.1 (#143)

BenchmarkGLM-4.7Llama 3.2 3B
Terminal-Bench33.4%—
Berkeley Function Calling Leaderboard—21.9%
BALROG—10.1%
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Llama 3.2 3B: 21.0 (#228)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Hard Prompts14431095
SimpleBench47.7%—
CritPt1.7%—
Chess Puzzles6%—
Epoch Capabilities Index143.51—

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Llama 3.2 3B: 32.4 (#214)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Math14231126
OTIS Mock AIME 2024-202583.3%—
ProofBench6%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Llama 3.2 3B: 29.7 (#235)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Expert14241090
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—

Multilingual GLM-4.7 leads

GLM-4.7: 52.8 (#79), Llama 3.2 3B: 26.2 (#281)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Non-English14171019
LMArena Chinese14951017
LMArena German14241056
LMArena Russian1423949
LMArena French1432—
LMArena Japanese1439—
LMArena Korean1399—
LMArena Spanish1434—

Instruction Following GLM-4.7 leads

GLM-4.7: 74.4 (#95), Llama 3.2 3B: 56.0 (#275)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Instruction Following14111089

Long Context GLM-4.7 leads

GLM-4.7: 42.8 (#116), Llama 3.2 3B: 33.4 (#261)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Longer Query14321100
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference GLM-4.7 leads

GLM-4.7: 60.9 (#93), Llama 3.2 3B: 24.7 (#307)

BenchmarkGLM-4.7Llama 3.2 3B
LMArena Text14351110
LMArena Creative Writing14011094
EQ-Bench Creative Writing1413595
LMArena Multi-Turn14461105

Frequently asked questions

Is GLM-4.7 better than Llama 3.2 3B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 28.9 on the Noometry Index. Llama 3.2 3B costs 8.3× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

Which is cheaper, GLM-4.7 or Llama 3.2 3B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

Is GLM-4.7 or Llama 3.2 3B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 27.6 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 131K.

How many benchmarks do GLM-4.7 and Llama 3.2 3B share?

14 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 3.2 3B has 18.

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