Model comparison

GLM-4.5V vs Llama 3-70B

GLM-4.5V is the stronger model overall, scoring 39.8 to 28.8 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Llama 3-70B Meta

28.8

Rank #323 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 3-70B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.5V leads 37.4 to 12.8.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 35.1% for Llama 3-70B.

Side by side

GLM-4.5V and Llama 3-70B specifications
GLM-4.5VLlama 3-70B
ProviderZ.ai (Zhipu)Meta
Noometry Index39.828.8
Released2025-08-112024-04-18
WeightsOpenOpen
Context window64K—
Max output16K—
Input $ / M tokens$0.60—
Output $ / M tokens$1.80—
Results tracked1531

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Llama 3-70B: 35.8 (#218)

Coding benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Coding13471206
BigCodeBench Instruct—43.6%
BigCodeBench Complete—54.5%
HumanEval+—72%
MBPP+—69%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Llama 3-70B: 21.1 (#139)

Agentic & Tool Use benchmarks
BenchmarkGLM-4.5VLlama 3-70B
Cybench—5%

Reasoning GLM-4.5V leads

GLM-4.5V: 27.4 (#119), Llama 3-70B: 18.0 (#288)

Reasoning benchmarks
BenchmarkGLM-4.5VLlama 3-70B
Kagi LLM Benchmark59.8%35.1%
LMArena Hard Prompts13341195
DTBench—54.2%
Epoch Capabilities Index—122.93
ForecastBench—57.1
WinoGrande—83.5%

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), Llama 3-70B: 12.8 (#305)

Math benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Math13541218
OTIS Mock AIME 2024-2025—4.3%
MATH Level 5—22.6%

Knowledge GLM-4.5V leads

GLM-4.5V: 37.5 (#156), Llama 3-70B: 20.8 (#277)

Knowledge benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Expert13531149
GPQA Diamond—40.6%
MMLU—79.3%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Llama 3-70B: —

Multimodal benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Vision1154—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.6 (#177), Llama 3-70B: 33.6 (#251)

Multilingual benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Non-English13031142
LMArena Chinese13371114
LMArena Russian12981159
LMArena Spanish13361241
LMArena French—1232
LMArena German—1169
LMArena Japanese—1017
LMArena Korean—1017

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.2 (#175), Llama 3-70B: 62.5 (#238)

Instruction Following benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Instruction Following13111194

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#171), Llama 3-70B: 35.6 (#240)

Long Context benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Longer Query13041174

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.5 (#170), Llama 3-70B: 42.8 (#231)

Writing & Preference benchmarks
BenchmarkGLM-4.5VLlama 3-70B
LMArena Text13331221
LMArena Creative Writing12951210
LMArena Multi-Turn13321223

Frequently asked questions

Is GLM-4.5V better than Llama 3-70B?

GLM-4.5V is the stronger model overall, scoring 39.8 to 28.8 on the Noometry Index.

Is GLM-4.5V or Llama 3-70B better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 35.8 in the Noometry coding category.

How many benchmarks do GLM-4.5V and Llama 3-70B share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 3-70B has 31.

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