# GLM-4.7-Flash vs Llama 3.2 3B

> GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 28.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-llama-3-2-3b
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 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-4.7-Flash leads 47.7 to 24.9.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 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 131K.

## Snapshot

| | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 38.4 | 28.4 |
| Rank | 175 | 318 |
| Context | 200K | 131K |
| Input $/M | $0.06 | $0.05 |
| Output $/M | $0.40 | $0.33 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.0 (#133)
- Llama 3.2 3B: 27.1 (#314)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1383 | 1098 |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- Llama 3.2 3B: 19.9 (#135)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |

## Reasoning

- GLM-4.7-Flash: 20.2 (#224)
- Llama 3.2 3B: 20.1 (#225)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1095 |
| Chess Puzzles | 0% | — |

## Math

- GLM-4.7-Flash: 34.4 (#159)
- Llama 3.2 3B: 29.9 (#213)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1354 | 1126 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |

## Knowledge

- GLM-4.7-Flash: 35.4 (#177)
- Llama 3.2 3B: 29.1 (#232)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1357 | 1090 |
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |

## Multilingual

- GLM-4.7-Flash: 46.8 (#152)
- Llama 3.2 3B: 26.4 (#276)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1331 | 1019 |
| LMArena Chinese | 1403 | 1017 |
| LMArena German | 1336 | 1056 |
| LMArena Russian | 1333 | 949 |
| LMArena French | 1332 | — |
| LMArena Korean | 1284 | — |
| LMArena Spanish | 1351 | — |

## Instruction Following

- GLM-4.7-Flash: 70.3 (#162)
- Llama 3.2 3B: 56.3 (#270)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1089 |

## Long Context

- GLM-4.7-Flash: 40.9 (#143)
- Llama 3.2 3B: 33.4 (#256)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1345 | 1101 |

## Writing & Preference

- GLM-4.7-Flash: 47.7 (#206)
- Llama 3.2 3B: 24.9 (#302)

| Benchmark | GLM-4.7-Flash | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1351 | 1110 |
| LMArena Creative Writing | 1297 | 1094 |
| EQ-Bench Creative Writing | 1125 | 595 |
| LMArena Multi-Turn | 1342 | 1105 |

## FAQ

### Is GLM-4.7-Flash better than Llama 3.2 3B?

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 28.4 on the Noometry Index.

### Which is cheaper, GLM-4.7-Flash 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-4.7-Flash lists at $0.06 and $0.40.

### Is GLM-4.7-Flash or Llama 3.2 3B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 27.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 131K.

### How many benchmarks do GLM-4.7-Flash and Llama 3.2 3B share?

14 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Llama 3.2 3B has 18.