# GLM-5.2 vs Qwen3.6 Plus

> GLM-5.2 is the stronger model overall, scoring 50.9 to 47.3 on the Noometry Index. Qwen3.6 Plus costs 1.9× 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-qwen3-6-plus
- Last updated: 2026-10-10
- Shared benchmarks: 33

## Summary

- They share 33 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Qwen3.6 Plus in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 28.5.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 59.2% for GLM-5.2 and 38.2% for Qwen3.6 Plus.
- Qwen3.6 Plus is cheaper at $0.50 / $3 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 47.3 |
| Rank | 38 | 54 |
| Context | 1M | 1M |
| Input $/M | $1.40 | $0.50 |
| Output $/M | $4.40 | $3 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.2: 50.9 (#37)
- Qwen3.6 Plus: 40.6 (#125)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| SWE-bench Verified | 78.7% | 57.9% |
| LMArena WebDev | 1604 | 1461 |
| SciCode | 50.5% | 40.7% |
| LMArena Coding | 1484 | 1467 |
| ALE-Bench | 1,047 | 670.15 |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| WeirdML | 70.1% | — |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- Qwen3.6 Plus: —

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| Vending-Bench 2 | 8,314 | 5,115 |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- Qwen3.6 Plus: 28.5 (#89)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| NYT Connections (extended) | 74.3% | 60.3% |
| CritPt | 20.9% | 2.9% |
| Chess Puzzles | 21% | 17% |
| LMArena Hard Prompts | 1480 | 1449 |
| Mystery Game Puzzles | 19% | 12% |
| DTBench | 93.6% | 81.9% |
| LMCA | 45.8% | 33.1% |
| Epoch Capabilities Index | 151.76 | 147.67 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| Thematic Generalization | — | 59.5% |
| EBR-Bench | 9.5% | — |
| Surface Evolver Bench | 55.6% | — |

## Math

- GLM-5.2: 55.4 (#35)
- Qwen3.6 Plus: 50.6 (#44)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 38.2% |
| OTIS Mock AIME 2024-2025 | 86.4% | 93.3% |
| LMArena Math | 1482 | 1450 |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| FrontierMath (Feb 2025 set) | — | 26.2% |
| FrontierMath Tier 4 (v1) | — | 8.3% |

## Knowledge

- GLM-5.2: 57.1 (#36)
- Qwen3.6 Plus: 56.1 (#40)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| GPQA Diamond | 91.9% | 88.4% |
| SimpleQA Verified | 34.2% | 44.1% |
| LMArena Expert | 1484 | 1456 |

## Multilingual

- GLM-5.2: 56.0 (#25)
- Qwen3.6 Plus: 53.5 (#66)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| LMArena Non-English | 1459 | 1424 |
| LMArena Chinese | 1518 | 1478 |
| LMArena French | 1476 | 1454 |
| LMArena German | 1470 | 1451 |
| LMArena Japanese | 1451 | 1389 |
| LMArena Korean | 1445 | 1381 |
| LMArena Russian | 1467 | 1434 |
| LMArena Spanish | 1477 | 1432 |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- Qwen3.6 Plus: 75.2 (#70)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| LMArena Instruction Following | 1465 | 1425 |

## Long Context

- GLM-5.2: 45.3 (#43)
- Qwen3.6 Plus: 45.1 (#48)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| LMArena Longer Query | 1479 | 1440 |
| CL-bench | — | 20.3% |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- Qwen3.6 Plus: 62.4 (#78)

| Benchmark | GLM-5.2 | Qwen3.6 Plus |
|---|---|---|
| LMArena Text | 1470 | 1437 |
| LMArena Creative Writing | 1465 | 1405 |
| LMArena Multi-Turn | 1468 | 1438 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than Qwen3.6 Plus?

GLM-5.2 is the stronger model overall, scoring 50.9 to 47.3 on the Noometry Index. Qwen3.6 Plus costs 1.9× 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 Qwen3.6 Plus?

Qwen3.6 Plus is cheaper. It lists at $0.50 per million input tokens and $3 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or Qwen3.6 Plus better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 40.6 in the Noometry coding category.

### Which has the bigger context window?

Both accept 1M tokens.

### How many benchmarks do GLM-5.2 and Qwen3.6 Plus share?

33 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen3.6 Plus has 37.