# GLM-5.3-Flash vs Qwen3.8 27B

> GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 46.0 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-qwen3-8-27b
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Qwen3.8 27B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 41.6.
- The biggest single-benchmark swing is ARC-AGI-2: 65.8% for GLM-5.3-Flash and 42.4% for Qwen3.8 27B.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- GLM-5.3-Flash accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 46.0 |
| Rank | 41 | 68 |
| Context | 1M | 262K |
| Input $/M | $0.15 | $0.99 |
| Output $/M | $0.50 | $1.49 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.1 (#31)
- Qwen3.8 27B: 50.5 (#44)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1609 | 1593 |
| SciCode | 51.6% | 46.6% |
| LMArena Coding | 1508 | 1482 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.2 (#47)
- Qwen3.8 27B: 32.9 (#57)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | 52.8% | 47.5% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 48.0 (#42)
- Qwen3.8 27B: 41.0 (#54)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 65.8% | 42.4% |
| ARC-AGI-1 | 91% | 87.5% |
| CritPt | 15.4% | 5.4% |
| LMArena Hard Prompts | 1491 | 1460 |
| Surface Evolver Bench | 52.5% | 45% |
| Epoch Capabilities Index | 151.88 | 149.38 |
| NYT Connections (extended) | — | 54.5% |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 88% |
| LMCA | — | 41.4% |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3-Flash: 53.3 (#47)
- Qwen3.8 27B: 37.1 (#161)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| ProofBench | 21% | 16% |
| LMArena Math | 1500 | 1456 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |

## Knowledge

- GLM-5.3-Flash: 58.4 (#36)
- Qwen3.8 27B: 41.6 (#109)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1513 | 1482 |
| GPQA Diamond | 90.2% | — |

## Multimodal

- GLM-5.3-Flash: 42.8 (#27)
- Qwen3.8 27B: 41.3 (#37)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1296 | 1271 |

## Multilingual

- GLM-5.3-Flash: 56.0 (#25)
- Qwen3.8 27B: 53.7 (#60)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1462 | 1430 |
| LMArena Chinese | 1527 | 1504 |
| LMArena French | 1496 | 1465 |
| LMArena German | 1470 | 1438 |
| LMArena Japanese | 1429 | 1384 |
| LMArena Korean | 1446 | 1393 |
| LMArena Russian | 1469 | 1415 |
| LMArena Spanish | 1471 | 1448 |

## Instruction Following

- GLM-5.3-Flash: 77.5 (#20)
- Qwen3.8 27B: 75.8 (#53)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1478 | 1439 |

## Long Context

- GLM-5.3-Flash: 45.4 (#39)
- Qwen3.8 27B: 44.3 (#70)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1482 | 1450 |

## Writing & Preference

- GLM-5.3-Flash: 65.3 (#50)
- Qwen3.8 27B: 65.8 (#43)

| Benchmark | GLM-5.3-Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1471 | 1441 |
| LMArena Creative Writing | 1442 | 1384 |
| LMArena Multi-Turn | 1467 | 1441 |
| EQ-Bench Creative Writing | — | 1671 |

## FAQ

### Is GLM-5.3-Flash better than Qwen3.8 27B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 46.0 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or Qwen3.8 27B?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.

### Is GLM-5.3-Flash or Qwen3.8 27B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 50.5 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.3-Flash and Qwen3.8 27B share?

27 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.8 27B has 31.