# GLM-5.3-Flash vs Qwen2.5-Coder-32B

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 32.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-qwen2-5-coder-32b
- Last updated: 2026-10-10
- Shared benchmarks: 13

## Summary

- They share 13 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3-Flash leads 53.7 to 22.4.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.66 / $1 for Qwen2.5-Coder-32B.
- GLM-5.3-Flash accepts more context: 1M tokens versus 33K.

## Snapshot

| | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 49.6 | 32.9 |
| Rank | 45 | 239 |
| Context | 1M | 33K |
| Input $/M | $0.15 | $0.66 |
| Output $/M | $0.50 | $1 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Qwen2.5-Coder-32B: 22.4 (#328)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1506 | 1276 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Qwen2.5-Coder-32B: —

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Qwen2.5-Coder-32B: 20.4 (#220)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1251 |
| Epoch Capabilities Index | 151.9 | 119.41 |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 8% | — |
| LiveBench Data Analysis | — | 49.9% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Qwen2.5-Coder-32B: 30.8 (#203)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1499 | 1251 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 46.6% |
| GSM8K | — | 93% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Qwen2.5-Coder-32B: 32.9 (#200)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1512 | 1221 |
| GPQA Diamond | 90.2% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Qwen2.5-Coder-32B: —

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Qwen2.5-Coder-32B: 38.0 (#230)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1462 | 1205 |
| LMArena Chinese | 1529 | 1222 |
| LMArena Russian | 1472 | 1228 |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Qwen2.5-Coder-32B: 61.6 (#240)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1223 |
| LiveBench Instruction Following | — | 58.7% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Qwen2.5-Coder-32B: 37.9 (#204)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1479 | 1251 |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Qwen2.5-Coder-32B: 41.8 (#235)

| Benchmark | GLM-5.3-Flash | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1471 | 1230 |
| LMArena Creative Writing | 1440 | 1174 |
| LMArena Multi-Turn | 1471 | 1222 |
| LiveBench Language | — | 23.3% |

## FAQ

### Is GLM-5.3-Flash better than Qwen2.5-Coder-32B?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 32.9 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or Qwen2.5-Coder-32B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen2.5-Coder-32B lists at $0.66 and $1.

### Is GLM-5.3-Flash or Qwen2.5-Coder-32B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 22.4 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 33K.

### How many benchmarks do GLM-5.3-Flash and Qwen2.5-Coder-32B share?

13 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen2.5-Coder-32B has 31.