# GLM-4.6 vs Qwen3 32B

> GLM-4.6 is the stronger model overall, scoring 40.9 to 38.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-qwen3-32b
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. GLM-4.6 scores higher in 5 categories and Qwen3 32B in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.3 to 53.1.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 48.7% for Qwen3 32B.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
- GLM-4.6 accepts more context: 205K tokens versus 131K.

## Snapshot

| | GLM-4.6 | Qwen3 32B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 38.8 |
| Rank | 132 | 166 |
| Context | 205K | 131K |
| Input $/M | $0.60 | $0.70 |
| Output $/M | $2.20 | $2.80 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- Qwen3 32B: 37.3 (#184)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| SciCode | 38.4% | 35.4% |
| LMArena Coding | 1449 | 1358 |
| SWE-bench Verified (bash only) | 55.4% | — |
| Aider Polyglot | — | 40% |
| LMArena WebDev | 1339 | — |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- Qwen3 32B: 32.3 (#58)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 48.7% |
| Terminal-Bench | 24.5% | — |

## Reasoning

- GLM-4.6: 23.1 (#168)
- Qwen3 32B: 19.6 (#233)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 54.9% |
| CritPt | 1.1% | 0.3% |
| LMArena Hard Prompts | 1440 | 1335 |
| Chess Puzzles | — | 5% |
| DTBench | — | 67.5% |
| LMCA | — | 17.3% |
| Epoch Capabilities Index | — | 138.51 |

## Math

- GLM-4.6: 36.4 (#115)
- Qwen3 32B: 37.9 (#89)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| LMArena Math | 1431 | 1399 |
| OTIS Mock AIME 2024-2025 | — | 66.9% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 39.6 (#118)
- Qwen3 32B: 40.0 (#112)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 5.9% |
| LMArena Expert | 1432 | 1362 |
| GPQA Diamond | — | 65.7% |

## Multilingual

- GLM-4.6: 53.6 (#62)
- Qwen3 32B: 45.8 (#162)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1426 | 1317 |
| LMArena Chinese | 1500 | 1357 |
| LMArena German | 1447 | 1341 |
| LMArena Russian | 1418 | 1311 |
| LMArena French | 1459 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1436 | — |

## Instruction Following

- GLM-4.6: 74.5 (#93)
- Qwen3 32B: 69.1 (#174)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1305 |

## Long Context

- GLM-4.6: 43.4 (#91)
- Qwen3 32B: 43.7 (#83)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| LMArena Longer Query | 1423 | 1327 |
| Fiction.LiveBench | — | 74.2% |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- Qwen3 32B: 53.1 (#158)

| Benchmark | GLM-4.6 | Qwen3 32B |
|---|---|---|
| LMArena Text | 1440 | 1340 |
| LMArena Creative Writing | 1411 | 1297 |
| LMArena Multi-Turn | 1427 | 1331 |
| EQ-Bench Creative Writing | 1411 | — |

## FAQ

### Is GLM-4.6 better than Qwen3 32B?

GLM-4.6 is the stronger model overall, scoring 40.9 to 38.8 on the Noometry Index.

### Which is cheaper, GLM-4.6 or Qwen3 32B?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

### Is GLM-4.6 or Qwen3 32B better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 37.3 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

### How many benchmarks do GLM-4.6 and Qwen3 32B share?

18 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3 32B has 26.