# GLM-5.3 vs Qwen3.5 27B

> GLM-5.3 is the stronger model overall, scoring 54.8 to 41.9 on the Noometry Index. Qwen3.5 27B costs 2.6× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-vs-qwen3-5-27b
- Last updated: 2026-10-12
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and Qwen3.5 27B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 38.8.
- The biggest single-benchmark swing is WeirdML: 75.4% for GLM-5.3 and 39.5% for Qwen3.5 27B.
- Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 54.8 | 41.9 |
| Rank | 26 | 127 |
| Context | 1M | 262K |
| Input $/M | $1.40 | $0.30 |
| Output $/M | $4.40 | $2.40 |
| Weights | Open | Open |

## Coding

- GLM-5.3: 59.5 (#14)
- Qwen3.5 27B: 38.9 (#168)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena WebDev | 1622 | 1358 |
| WeirdML | 75.4% | 39.5% |
| LMArena Coding | 1496 | 1427 |
| ALE-Bench | 1,317 | 349.45 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |

## Agentic & Tool Use

- GLM-5.3: 36.4 (#38)
- Qwen3.5 27B: —

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| Vending-Bench 2 | 8,164 | 201.98 |
| APEX-Agents | 56.6% | — |

## Reasoning

- GLM-5.3: 46.1 (#46)
- Qwen3.5 27B: 27.5 (#117)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| NYT Connections (extended) | 74.2% | 47.9% |
| LMArena Hard Prompts | 1489 | 1414 |
| DTBench | 87.7% | 82.4% |
| LMCA | 55.5% | 34% |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 45.5% |
| Mystery Game Puzzles | 33% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |

## Math

- GLM-5.3: 62.3 (#33)
- Qwen3.5 27B: 38.8 (#127)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Math | 1489 | 1429 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |

## Knowledge

- GLM-5.3: 58.3 (#37)
- Qwen3.5 27B: 38.0 (#150)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Expert | 1516 | 1428 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 12.1% |

## Multimodal

- GLM-5.3: —
- Qwen3.5 27B: 39.4 (#59)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | — | 1241 |

## Multilingual

- GLM-5.3: 55.7 (#28)
- Qwen3.5 27B: 50.8 (#115)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1457 | 1390 |
| LMArena Chinese | 1528 | 1478 |
| LMArena French | 1499 | 1410 |
| LMArena German | 1499 | 1393 |
| LMArena Japanese | 1453 | 1345 |
| LMArena Korean | 1472 | 1358 |
| LMArena Russian | 1463 | 1390 |
| LMArena Spanish | 1460 | 1407 |

## Instruction Following

- GLM-5.3: 77.5 (#23)
- Qwen3.5 27B: 73.5 (#119)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1477 | 1393 |

## Long Context

- GLM-5.3: 45.4 (#41)
- Qwen3.5 27B: 43.1 (#106)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1482 | 1413 |

## Writing & Preference

- GLM-5.3: 75.7 (#6)
- Qwen3.5 27B: 59.3 (#111)

| Benchmark | GLM-5.3 | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1471 | 1409 |
| LMArena Creative Writing | 1457 | 1362 |
| LMArena Multi-Turn | 1472 | 1410 |
| EQ-Bench Creative Writing | 2075 | — |

## FAQ

### Is GLM-5.3 better than Qwen3.5 27B?

GLM-5.3 is the stronger model overall, scoring 54.8 to 41.9 on the Noometry Index. Qwen3.5 27B costs 2.6× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.3 or Qwen3.5 27B?

Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

### Is GLM-5.3 or Qwen3.5 27B better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 38.9 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do GLM-5.3 and Qwen3.5 27B share?

24 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Qwen3.5 27B has 28.