# GLM-5.2 vs Qwen3.5 27B

> GLM-5.2 is the stronger model overall, scoring 51.1 to 41.9 on the Noometry Index. Qwen3.5 27B costs 2.6× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-2-vs-qwen3-5-27b
- Last updated: 2026-10-12
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Qwen3.5 27B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 38.0.
- The biggest single-benchmark swing is WeirdML: 70.1% for GLM-5.2 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-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.1 | 41.9 |
| Rank | 44 | 127 |
| Context | 1M | 262K |
| Input $/M | $1.40 | $0.30 |
| Output $/M | $4.40 | $2.40 |
| Weights | Open | Open |

## Coding

- GLM-5.2: 51.3 (#41)
- Qwen3.5 27B: 38.9 (#168)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena WebDev | 1603 | 1358 |
| WeirdML | 70.1% | 39.5% |
| LMArena Coding | 1485 | 1427 |
| ALE-Bench | 1,047 | 349.45 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SciCode | 50.5% | — |

## Agentic & Tool Use

- GLM-5.2: 32.4 (#63)
- Qwen3.5 27B: —

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| Vending-Bench 2 | 8,314 | 201.98 |
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |

## Reasoning

- GLM-5.2: 42.3 (#52)
- Qwen3.5 27B: 27.5 (#117)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| NYT Connections (extended) | 74.3% | 47.9% |
| LMArena Hard Prompts | 1480 | 1414 |
| DTBench | 93.6% | 82.4% |
| LMCA | 45.8% | 34% |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 45.5% |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |

## Math

- GLM-5.2: 55.7 (#43)
- Qwen3.5 27B: 38.8 (#127)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| MathArena Final-Answer Competitions | 67.6% | 56.7% |
| LMArena Math | 1482 | 1429 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |

## Knowledge

- GLM-5.2: 57.1 (#40)
- Qwen3.5 27B: 38.0 (#150)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena Expert | 1486 | 1428 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 12.1% |

## Multimodal

- GLM-5.2: —
- Qwen3.5 27B: 39.4 (#59)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | — | 1241 |

## Multilingual

- GLM-5.2: 55.8 (#26)
- Qwen3.5 27B: 50.8 (#115)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1459 | 1390 |
| LMArena Chinese | 1519 | 1478 |
| LMArena French | 1479 | 1410 |
| LMArena German | 1468 | 1393 |
| LMArena Japanese | 1451 | 1345 |
| LMArena Korean | 1445 | 1358 |
| LMArena Russian | 1466 | 1390 |
| LMArena Spanish | 1477 | 1407 |

## Instruction Following

- GLM-5.2: 76.9 (#34)
- Qwen3.5 27B: 73.5 (#119)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1465 | 1393 |

## Long Context

- GLM-5.2: 45.3 (#43)
- Qwen3.5 27B: 43.1 (#106)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1479 | 1413 |

## Writing & Preference

- GLM-5.2: 70.4 (#21)
- Qwen3.5 27B: 59.3 (#111)

| Benchmark | GLM-5.2 | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1470 | 1409 |
| LMArena Creative Writing | 1462 | 1362 |
| LMArena Multi-Turn | 1469 | 1410 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than Qwen3.5 27B?

GLM-5.2 is the stronger model overall, scoring 51.1 to 41.9 on the Noometry Index. Qwen3.5 27B costs 2.6× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.2 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-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or Qwen3.5 27B better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 38.9 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.2 and Qwen3.5 27B share?

25 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Qwen3.5 27B has 28.