# GLM-5.3-Flash vs Qwen-14B

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-qwen-14b
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. GLM-5.3-Flash scores higher in 7 categories and Qwen-14B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3-Flash leads 65.4 to 27.8.

## Snapshot

| | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 49.6 | 30.8 |
| Rank | 45 | 273 |
| Context | 1M | — |
| Input $/M | $0.15 | — |
| Output $/M | $0.50 | — |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Qwen-14B: 30.6 (#283)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Coding | 1506 | 1072 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Qwen-14B: —

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Qwen-14B: 18.7 (#255)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1027 |
| Epoch Capabilities Index | 151.9 | 112.8 |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| BIG-Bench Hard | — | 55% |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Qwen-14B: 28.7 (#224)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Math | 1499 | 1068 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| GSM8K | — | 61.3% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Qwen-14B: —

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| GPQA Diamond | 90.2% | — |
| LMArena Expert | 1512 | — |
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Qwen-14B: —

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Qwen-14B: 27.7 (#270)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1462 | 1042 |
| LMArena Chinese | 1529 | 1077 |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Russian | 1472 | — |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Qwen-14B: 52.6 (#284)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1031 |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Qwen-14B: 31.3 (#276)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1479 | 1029 |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Qwen-14B: 27.8 (#294)

| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Text | 1471 | 1051 |
| LMArena Creative Writing | 1440 | 1028 |
| LMArena Multi-Turn | 1471 | 1022 |

## FAQ

### Is GLM-5.3-Flash better than Qwen-14B?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.8 on the Noometry Index.

### Is GLM-5.3-Flash or Qwen-14B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 30.6 in the Noometry coding category.

### How many benchmarks do GLM-5.3-Flash and Qwen-14B share?

11 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen-14B has 18.