# GLM-5 vs Qwen3 32B

> GLM-5 is the stronger model overall, scoring 46.1 to 39.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-vs-qwen3-32b
- Last updated: 2026-10-11
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. GLM-5 scores higher in 8 categories and Qwen3 32B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5 leads 66.0 to 52.9.
- The biggest single-benchmark swing is GPQA Diamond: 87.8% for GLM-5 and 65.7% for Qwen3 32B.
- Qwen3 32B is cheaper at $0.70 / $2.80 per million input/output tokens, against $1 / $3.20 for GLM-5.
- GLM-5 accepts more context: 205K tokens versus 131K.

## Snapshot

| | GLM-5 | Qwen3 32B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 46.1 | 39.2 |
| Rank | 66 | 172 |
| Context | 205K | 131K |
| Input $/M | $1 | $0.70 |
| Output $/M | $3.20 | $2.80 |
| Weights | Open | Open |

## Coding

- GLM-5: 49.0 (#52)
- Qwen3 32B: 37.7 (#190)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| LMArena Coding | 1461 | 1358 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| Aider Polyglot | — | 40% |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 35.4% |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |

## Agentic & Tool Use

- GLM-5: 31.1 (#71)
- Qwen3 32B: 32.6 (#62)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| Berkeley Function Calling Leaderboard | — | 48.7% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |

## Reasoning

- GLM-5: 27.6 (#116)
- Qwen3 32B: 20.2 (#241)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| Kagi LLM Benchmark | 75% | 54.9% |
| Chess Puzzles | 10% | 5% |
| LMArena Hard Prompts | 1452 | 1334 |
| Epoch Capabilities Index | 145.83 | 138.51 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 0.3% |
| DTBench | — | 67.5% |
| LMCA | — | 17.3% |
| ForecastBench | 61 | — |

## Math

- GLM-5: 46.4 (#71)
- Qwen3 32B: 39.7 (#99)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 66.9% |
| LMArena Math | 1440 | 1399 |
| MathArena Final-Answer Competitions | 65.7% | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-5: 52.3 (#64)
- Qwen3 32B: 40.0 (#125)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 87.8% | 65.7% |
| Vectara Hallucination Rate | 10.1% | 5.9% |
| LMArena Expert | 1454 | 1362 |

## Multilingual

- GLM-5: 53.7 (#58)
- Qwen3 32B: 45.6 (#167)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1430 | 1317 |
| LMArena Chinese | 1511 | 1357 |
| LMArena German | 1445 | 1341 |
| LMArena Russian | 1436 | 1311 |
| LMArena French | 1455 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
| LMArena Spanish | 1454 | — |

## Instruction Following

- GLM-5: 75.2 (#67)
- Qwen3 32B: 68.9 (#179)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1428 | 1305 |

## Long Context

- GLM-5: 44.7 (#60)
- Qwen3 32B: 43.8 (#87)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| LMArena Longer Query | 1446 | 1327 |
| Fiction.LiveBench | — | 74.2% |
| CL-bench | 18.7% | — |

## Writing & Preference

- GLM-5: 66.0 (#38)
- Qwen3 32B: 52.9 (#163)

| Benchmark | GLM-5 | Qwen3 32B |
|---|---|---|
| LMArena Text | 1446 | 1340 |
| LMArena Creative Writing | 1439 | 1297 |
| LMArena Multi-Turn | 1456 | 1331 |
| EQ-Bench Creative Writing | 1601 | — |

## FAQ

### Is GLM-5 better than Qwen3 32B?

GLM-5 is the stronger model overall, scoring 46.1 to 39.2 on the Noometry Index.

### Which is cheaper, GLM-5 or Qwen3 32B?

Qwen3 32B is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; GLM-5 lists at $1 and $3.20.

### Is GLM-5 or Qwen3 32B better for coding?

GLM-5 scores higher on coding benchmarks: 49.0 versus 37.7 in the Noometry coding category.

### Which has the bigger context window?

GLM-5 does, with 205K tokens against 131K.

### How many benchmarks do GLM-5 and Qwen3 32B share?

19 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3 32B has 26.