Noometry

Model comparison

GLM-4.5V vs Llama 3.1-70B

GLM-4.5V is the stronger model overall, scoring 39.2 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.2× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.2

Rank #158 Confirmed

Llama 3.1-70B Meta

29.4

Rank #299 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 3.1-70B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.5V leads 34.7 to 12.7.
  • Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
  • Llama 3.1-70B accepts more context: 128K tokens versus 64K.

Side by side

GLM-4.5VLlama 3.1-70B
ProviderZ.ai (Zhipu)Meta
Noometry Index39.229.4
Released2025-08-112024-07-23
WeightsOpenOpen
Context window64K128K
Max output16K4K
Input $ / M tokens$0.60$0.40
Output $ / M tokens$1.80$0.40
Results tracked1535

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 38.9 (#152), Llama 3.1-70B: 29.8 (#292)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Coding13471260
WeirdML—9%
BigCodeBench Instruct—46.1%
BigCodeBench Complete—54.8%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Llama 3.1-70B: 24.9 (#104)

BenchmarkGLM-4.5VLlama 3.1-70B
TheAgentCompany—6.9%
BALROG—27.9%

Reasoning GLM-4.5V leads

GLM-4.5V: 26.5 (#118), Llama 3.1-70B: 20.8 (#215)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Hard Prompts13351241
Kagi LLM Benchmark59.8%—
DTBench—60%
LMCA—14.8%
Epoch Capabilities Index—125.91

Math GLM-4.5V leads

GLM-4.5V: 34.7 (#157), Llama 3.1-70B: 12.7 (#298)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Math13531252
OTIS Mock AIME 2024-2025—3.6%
Omni-MATH—21%
MATH Level 5—36.7%

Knowledge GLM-4.5V leads

GLM-4.5V: 36.8 (#155), Llama 3.1-70B: 24.1 (#264)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Expert13531209
GPQA Diamond—44.2%
MMLU-Pro—65.3%
GPQA (HELM)—42.6%
MMLU—80.1%

Multimodal Not comparable

GLM-4.5V: 33.6 (#86), Llama 3.1-70B: —

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Vision1154—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.9 (#172), Llama 3.1-70B: 38.9 (#220)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Non-English13041219
LMArena Chinese13381215
LMArena Russian12991234
LMArena Spanish13351253
LMArena French—1260
LMArena German—1222
LMArena Japanese—1132
LMArena Korean—1140

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.4 (#170), Llama 3.1-70B: 65.5 (#218)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Instruction Following13111231
IFEval—82.1%

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#166), Llama 3.1-70B: 37.6 (#208)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Longer Query13051241

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.7 (#162), Llama 3.1-70B: 35.6 (#262)

BenchmarkGLM-4.5VLlama 3.1-70B
LMArena Text13331261
LMArena Creative Writing12951232
LMArena Multi-Turn13331256
EQ-Bench Creative Writing—784
WildBench—75.8%

Frequently asked questions

Is GLM-4.5V better than Llama 3.1-70B?

GLM-4.5V is the stronger model overall, scoring 39.2 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.2× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5V or Llama 3.1-70B?

Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

Is GLM-4.5V or Llama 3.1-70B better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 29.8 in the Noometry coding category.

Which has the bigger context window?

Llama 3.1-70B does, with 128K tokens against 64K.

How many benchmarks do GLM-4.5V and Llama 3.1-70B share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 3.1-70B has 35.

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