# GLM-4.6V vs Llama 3.2 3B

> GLM-4.6V is the stronger model overall, scoring 41.3 to 28.9 on the Noometry Index. Llama 3.2 3B costs 3.7× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-llama-3-2-3b
- Last updated: 2026-10-12
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3.2 3B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.6 to 24.7.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- Llama 3.2 3B accepts more context: 131K tokens versus 128K.

## Snapshot

| | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 28.9 |
| Rank | 137 | 321 |
| Context | 128K | 131K |
| Input $/M | $0.30 | $0.05 |
| Output $/M | $0.90 | $0.33 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.9 (#128)
- Llama 3.2 3B: 27.6 (#319)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1390 | 1098 |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |

## Agentic & Tool Use

- GLM-4.6V: —
- Llama 3.2 3B: 20.1 (#143)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |

## Reasoning

- GLM-4.6V: 27.6 (#115)
- Llama 3.2 3B: 21.0 (#228)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1095 |

## Math

- GLM-4.6V: —
- Llama 3.2 3B: 32.4 (#214)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Math | — | 1126 |

## Knowledge

- GLM-4.6V: 38.0 (#149)
- Llama 3.2 3B: 29.7 (#235)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1371 | 1090 |

## Multimodal

- GLM-4.6V: 34.8 (#90)
- Llama 3.2 3B: —

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.6 (#141)
- Llama 3.2 3B: 26.2 (#281)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1359 | 1019 |
| LMArena Chinese | 1425 | 1017 |
| LMArena Russian | 1340 | 949 |
| LMArena German | — | 1056 |

## Instruction Following

- GLM-4.6V: 71.4 (#151)
- Llama 3.2 3B: 56.0 (#275)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1352 | 1089 |

## Long Context

- GLM-4.6V: 41.3 (#143)
- Llama 3.2 3B: 33.4 (#261)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1358 | 1100 |

## Writing & Preference

- GLM-4.6V: 56.6 (#137)
- Llama 3.2 3B: 24.7 (#307)

| Benchmark | GLM-4.6V | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1377 | 1110 |
| LMArena Creative Writing | 1347 | 1094 |
| LMArena Multi-Turn | 1360 | 1105 |
| EQ-Bench Creative Writing | — | 595 |

## FAQ

### Is GLM-4.6V better than Llama 3.2 3B?

GLM-4.6V is the stronger model overall, scoring 41.3 to 28.9 on the Noometry Index. Llama 3.2 3B costs 3.7× 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 3.2 3B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

### Is GLM-4.6V or Llama 3.2 3B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 27.6 in the Noometry coding category.

### Which has the bigger context window?

Llama 3.2 3B does, with 131K tokens against 128K.

### How many benchmarks do GLM-4.6V and Llama 3.2 3B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.2 3B has 18.