Noometry

Model comparison

GLM-4.5V vs Llama 3.1-405B

GLM-4.5V is the stronger model overall, scoring 39.2 to 30.4 on the Noometry Index.

Last verified . 14 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.2

Rank #158 Confirmed

Llama 3.1-405B Meta

30.4

Rank #279 Confirmed

Summary

  • They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 3.1-405B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.5V leads 34.7 to 17.3.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 45% for Llama 3.1-405B.

Side by side

GLM-4.5VLlama 3.1-405B
ProviderZ.ai (Zhipu)Meta
Noometry Index39.230.4
Released2025-08-112024-07-23
WeightsOpenOpen
Context window64K—
Max output16K—
Input $ / M tokens$0.60—
Output $ / M tokens$1.80—
Results tracked1542

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 38.9 (#152), Llama 3.1-405B: 32.7 (#257)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Coding13471291
WeirdML—21.4%

Agentic & Tool Use Not comparable

GLM-4.5V: —, Llama 3.1-405B: 20.9 (#131)

BenchmarkGLM-4.5VLlama 3.1-405B
TheAgentCompany—7.4%
Cybench—7.5%

Reasoning GLM-4.5V leads

GLM-4.5V: 26.5 (#118), Llama 3.1-405B: 16.4 (#293)

BenchmarkGLM-4.5VLlama 3.1-405B
Kagi LLM Benchmark59.8%45%
LMArena Hard Prompts13351270
SimpleBench—23%
DTBench—61.4%
BIG-Bench Hard—82.9%
Epoch Capabilities Index—128.76
ForecastBench—59.9
HellaSwag—89.2%
PIQA—85.9%
WinoGrande—89.2%

Math GLM-4.5V leads

GLM-4.5V: 34.7 (#157), Llama 3.1-405B: 17.3 (#283)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Math13531281
OTIS Mock AIME 2024-2025—9.7%
Omni-MATH—24.9%
MATH Level 5—49.8%

Knowledge GLM-4.5V leads

GLM-4.5V: 36.8 (#155), Llama 3.1-405B: 30.4 (#221)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Expert13531243
GPQA Diamond—50.9%
MMLU-Pro—72.3%
Confabulations—17.6%
GPQA (HELM)—52.2%
ARC (AI2) Challenge—95.3%
MMLU—84.5%
TriviaQA—82.7%

Multimodal Not comparable

GLM-4.5V: 33.6 (#86), Llama 3.1-405B: —

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Vision1154—

Multilingual GLM-4.5V leads

GLM-4.5V: 44.9 (#172), Llama 3.1-405B: 40.9 (#209)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Non-English13041248
LMArena Chinese13381242
LMArena Russian12991265
LMArena Spanish13351259
LMArena French—1278
LMArena German—1252
LMArena Japanese—1208
LMArena Korean—1184

Instruction Following GLM-4.5V leads

GLM-4.5V: 69.4 (#170), Llama 3.1-405B: 66.1 (#209)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Instruction Following13111259
IFEval—81.1%

Long Context GLM-4.5V leads

GLM-4.5V: 39.6 (#166), Llama 3.1-405B: 38.4 (#192)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Longer Query13051266

Writing & Preference GLM-4.5V leads

GLM-4.5V: 52.7 (#162), Llama 3.1-405B: 39.1 (#246)

BenchmarkGLM-4.5VLlama 3.1-405B
LMArena Text13331284
LMArena Creative Writing12951262
LMArena Multi-Turn13331297
EQ-Bench Creative Writing—870
WildBench—78.3%

Frequently asked questions

Is GLM-4.5V better than Llama 3.1-405B?

GLM-4.5V is the stronger model overall, scoring 39.2 to 30.4 on the Noometry Index.

Is GLM-4.5V or Llama 3.1-405B better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 32.7 in the Noometry coding category.

How many benchmarks do GLM-4.5V and Llama 3.1-405B share?

14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 3.1-405B has 42.

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