Model comparison

GLM-4.6 vs Llama 13b

GLM-4.6 is the stronger model overall, scoring 41.4 to 24.4 on the Noometry Index.

Last verified . 8 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Llama 13b Meta

24.4

Rank #348 Confirmed

Summary

  • They share 8 benchmarks with published results for both. GLM-4.6 scores higher in 6 categories and Llama 13b in 0 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 13.8.

Side by side

GLM-4.6 and Llama 13b specifications
GLM-4.6Llama 13b
ProviderZ.ai (Zhipu)Meta
Noometry Index41.424.4
Released2025-09-302023-02-24
WeightsOpenOpen
Context window205K—
Max output131K—
Input $ / M tokens$0.60—
Output $ / M tokens$2.20—
Results tracked2921

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Llama 13b: 21.4 (#337)

Coding benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Coding1449683
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
ALE-Bench340.82—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Llama 13b: —

Agentic & Tool Use benchmarks
BenchmarkGLM-4.6Llama 13b
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), Llama 13b: 14.0 (#329)

Reasoning benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Hard Prompts1440728
Kagi LLM Benchmark47.4%—
CritPt1.1%—
BIG-Bench Hard—37.9%
Epoch Capabilities Index—100.58
HellaSwag—79.2%
LAMBADA—75.2%
PIQA—80.1%
WinoGrande—73%

Math GLM-4.6 leads

GLM-4.6: 39.1 (#111), Llama 13b: 26.7 (#256)

Math benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Math1432838
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—
GSM8K—20.6%

Knowledge Not comparable

GLM-4.6: 40.2 (#124), Llama 13b: —

Knowledge benchmarks
BenchmarkGLM-4.6Llama 13b
Vectara Hallucination Rate9.5%—
LMArena Expert1431—
ARC (AI2) Challenge—52.7%
BoolQ—78.7%
MMLU—47.7%
OpenBookQA—56.4%
TriviaQA—77.9%

Multimodal Not comparable

GLM-4.6: —, Llama 13b: —

Multimodal benchmarks
BenchmarkGLM-4.6Llama 13b
ScienceQA—43.3%

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Llama 13b: 16.6 (#297)

Multilingual benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Non-English1426819
LMArena Chinese1499—
LMArena French1459—
LMArena German1447—
LMArena Japanese1393—
LMArena Korean1400—
LMArena Russian1419—
LMArena Spanish1436—

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Llama 13b: 36.7 (#305)

Instruction Following benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Instruction Following1410781

Long Context Not comparable

GLM-4.6: 43.4 (#94), Llama 13b: —

Long Context benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Longer Query1422—

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Llama 13b: 13.8 (#312)

Writing & Preference benchmarks
BenchmarkGLM-4.6Llama 13b
LMArena Text1440834
LMArena Creative Writing1411794
LMArena Multi-Turn1427753
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than Llama 13b?

GLM-4.6 is the stronger model overall, scoring 41.4 to 24.4 on the Noometry Index.

Is GLM-4.6 or Llama 13b better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 21.4 in the Noometry coding category.

How many benchmarks do GLM-4.6 and Llama 13b share?

8 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Llama 13b has 21.

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