Noometry

Model comparison

GLM-4.6V vs Llama 3.1-8B

GLM-4.6V is the stronger model overall, scoring 40.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 7.8× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

Last verified . 11 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Llama 3.1-8B Meta

22.8

Rank #347 Confirmed

Summary

  • They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3.1-8B in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 8.0.
  • Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.

Side by side

GLM-4.6VLlama 3.1-8B
ProviderZ.ai (Zhipu)Meta
Noometry Index40.922.8
Released2025-12-082024-07-23
WeightsOpenOpen
Context window128K128K
Max output33K4K
Input $ / M tokens$0.30$0.05
Output $ / M tokens$0.90$0.08
Results tracked1243

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Category by category

Coding GLM-4.6V leads

GLM-4.6V: 40.2 (#128), Llama 3.1-8B: 19.9 (#335)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Coding13901195
SciCode—13.2%
WeirdML—1.7%
BigCodeBench Instruct—32.8%
BigCodeBench Complete—40.5%
HumanEval+—62.8%
MBPP+—55.6%

Agentic & Tool Use Not comparable

GLM-4.6V: —, Llama 3.1-8B: 22.3 (#123)

BenchmarkGLM-4.6VLlama 3.1-8B
Berkeley Function Calling Leaderboard—25.8%
BALROG—15.1%

Reasoning GLM-4.6V leads

GLM-4.6V: 26.5 (#117), Llama 3.1-8B: 14.5 (#316)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Hard Prompts13681175
CritPt—0%
Chess Puzzles—0%
DTBench—50.9%
LMCA—5.4%
Epoch Capabilities Index—116.48
PIQA—81.2%

Math Not comparable

GLM-4.6V: —, Llama 3.1-8B: 9.5 (#311)

BenchmarkGLM-4.6VLlama 3.1-8B
OTIS Mock AIME 2024-2025—1.7%
Omni-MATH—13.7%
LMArena Math—1179
MATH Level 5—22.9%
GSM8K—82.4%

Knowledge GLM-4.6V leads

GLM-4.6V: 37.4 (#148), Llama 3.1-8B: 8.0 (#302)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Expert13721144
GPQA Diamond—27%
MMLU-Pro—40.6%
GPQA (HELM)—24.7%
BoolQ—82.8%
MMLU—56.1%

Multimodal Not comparable

GLM-4.6V: 34.2 (#83), Llama 3.1-8B: —

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Vision1164—

Multilingual GLM-4.6V leads

GLM-4.6V: 48.8 (#135), Llama 3.1-8B: 34.2 (#244)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Non-English13591148
LMArena Chinese14261151
LMArena Russian13411158
LMArena French—1176
LMArena German—1144
LMArena Japanese—1061
LMArena Korean—1053
LMArena Spanish—1169

Instruction Following GLM-4.6V leads

GLM-4.6V: 71.6 (#145), Llama 3.1-8B: 59.1 (#253)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Instruction Following13511159
IFEval—74.3%

Long Context GLM-4.6V leads

GLM-4.6V: 41.3 (#138), Llama 3.1-8B: 35.8 (#233)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Longer Query13581182

Writing & Preference GLM-4.6V leads

GLM-4.6V: 56.8 (#132), Llama 3.1-8B: 29.9 (#285)

BenchmarkGLM-4.6VLlama 3.1-8B
LMArena Text13771187
LMArena Creative Writing13471154
LMArena Multi-Turn13591172
EQ-Bench Creative Writing—713
WildBench—68.7%

Frequently asked questions

Is GLM-4.6V better than Llama 3.1-8B?

GLM-4.6V is the stronger model overall, scoring 40.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 7.8× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

Which is cheaper, GLM-4.6V or Llama 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

Is GLM-4.6V or Llama 3.1-8B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 19.9 in the Noometry coding category.

Which has the bigger context window?

Both accept 128K tokens.

How many benchmarks do GLM-4.6V and Llama 3.1-8B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.1-8B has 43.

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