# GLM-4.6V vs Qwen2.5 32B Instruct

> GLM-4.6V is the stronger model overall, scoring 40.9 to 29.7 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-qwen2-5-32b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 24.9.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
- Qwen2.5 32B Instruct accepts more context: 131K tokens versus 128K.

## Snapshot

| | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 29.7 |
| Rank | 133 | 291 |
| Context | 128K | 131K |
| Input $/M | $0.30 | $0.70 |
| Output $/M | $0.90 | $2.80 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.2 (#128)
- Qwen2.5 32B Instruct: 38.1 (#165)

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1390 | — |
| BigCodeBench Complete | — | 52.3% |

## Reasoning

- GLM-4.6V: 26.5 (#117)
- Qwen2.5 32B Instruct: 18.5 (#262)

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1368 | — |
| Epoch Capabilities Index | — | 128.52 |

## Math

- GLM-4.6V: —
- Qwen2.5 32B Instruct: 15.3 (#289)

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.4% |
| MATH Level 5 | — | 56.1% |

## Knowledge

- GLM-4.6V: 37.4 (#148)
- Qwen2.5 32B Instruct: 24.9 (#259)

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | — | 46.1% |
| LMArena Expert | 1372 | — |

## Multimodal

- GLM-4.6V: 34.2 (#83)
- Qwen2.5 32B Instruct: —

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.8 (#135)
- Qwen2.5 32B Instruct: —

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1426 | — |
| LMArena Russian | 1341 | — |

## Instruction Following

- GLM-4.6V: 71.6 (#145)
- Qwen2.5 32B Instruct: —

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1351 | — |

## Long Context

- GLM-4.6V: 41.3 (#138)
- Qwen2.5 32B Instruct: —

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1358 | — |

## Writing & Preference

- GLM-4.6V: 56.8 (#132)
- Qwen2.5 32B Instruct: —

| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| LMArena Multi-Turn | 1359 | — |

## FAQ

### Is GLM-4.6V better than Qwen2.5 32B Instruct?

GLM-4.6V is the stronger model overall, scoring 40.9 to 29.7 on the Noometry Index.

### Which is cheaper, GLM-4.6V or Qwen2.5 32B Instruct?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.

### Is GLM-4.6V or Qwen2.5 32B Instruct better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 38.1 in the Noometry coding category.

### Which has the bigger context window?

Qwen2.5 32B Instruct does, with 131K tokens against 128K.

### How many benchmarks do GLM-4.6V and Qwen2.5 32B Instruct share?

0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen2.5 32B Instruct has 7.