Noometry

Model comparison

GLM-4.7-Flash vs Llama 3.2 1B

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.

Last verified . 17 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Llama 3.2 1B Meta

20.1

Rank #354 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Llama 3.2 1B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7-Flash leads 35.5 to 7.2.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 0.6% for Llama 3.2 1B.
  • Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.06 / $0.40 for GLM-4.7-Flash.
  • GLM-4.7-Flash accepts more context: 200K tokens versus 60K.

Side by side

GLM-4.7-FlashLlama 3.2 1B
ProviderZ.ai (Zhipu)Meta
Noometry Index38.820.1
Released2026-01-192024-09-24
WeightsOpenOpen
Context window200K60K
Max output131K54K
Input $ / M tokens$0.06$0.027
Output $ / M tokens$0.40$0.20
Results tracked2122

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Llama 3.2 1B: 21.1 (#338)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
LMArena Coding13831070
BigCodeBench Instruct—8.2%
BigCodeBench Complete—11.3%

Agentic & Tool Use Not comparable

GLM-4.7-Flash: —, Llama 3.2 1B: 14.6 (#150)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
Berkeley Function Calling Leaderboard—10.8%
BALROG—6.6%

Reasoning GLM-4.7-Flash leads

GLM-4.7-Flash: 20.9 (#229), Llama 3.2 1B: 16.2 (#308)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
Chess Puzzles0%0%
LMArena Hard Prompts13561044
Epoch Capabilities Index—101.99

Math GLM-4.7-Flash leads

GLM-4.7-Flash: 36.1 (#173), Llama 3.2 1B: 10.4 (#313)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
OTIS Mock AIME 2024-202558.3%0.6%
LMArena Math13551086

Knowledge GLM-4.7-Flash leads

GLM-4.7-Flash: 35.5 (#184), Llama 3.2 1B: 7.2 (#312)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
GPQA Diamond60.5%23.9%
LMArena Expert13571007
Vectara Hallucination Rate9.3%—

Multilingual GLM-4.7-Flash leads

GLM-4.7-Flash: 46.5 (#158), Llama 3.2 1B: 23.8 (#292)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
LMArena Non-English1330973
LMArena Chinese1403959
LMArena German13371014
LMArena Russian1332941
LMArena French1332—
LMArena Korean1283—
LMArena Spanish1350—

Instruction Following GLM-4.7-Flash leads

GLM-4.7-Flash: 70.1 (#167), Llama 3.2 1B: 52.4 (#290)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
LMArena Instruction Following13271031

Long Context GLM-4.7-Flash leads

GLM-4.7-Flash: 40.9 (#148), Llama 3.2 1B: 31.9 (#274)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
LMArena Longer Query13451050

Writing & Preference GLM-4.7-Flash leads

GLM-4.7-Flash: 47.4 (#210), Llama 3.2 1B: 21.3 (#310)

BenchmarkGLM-4.7-FlashLlama 3.2 1B
LMArena Text13511055
LMArena Creative Writing12971033
EQ-Bench Creative Writing1125200
LMArena Multi-Turn13421030

Frequently asked questions

Is GLM-4.7-Flash better than Llama 3.2 1B?

GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 20.1 on the Noometry Index. Llama 3.2 1B costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.

Which is cheaper, GLM-4.7-Flash or Llama 3.2 1B?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; GLM-4.7-Flash lists at $0.06 and $0.40.

Is GLM-4.7-Flash or Llama 3.2 1B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 21.1 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 60K.

How many benchmarks do GLM-4.7-Flash and Llama 3.2 1B share?

17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Llama 3.2 1B has 22.

Related comparisons

Go deeper