# GLM-4.7 vs Qwen3.5-Flash

> GLM-4.7 and Qwen3.5-Flash score almost the same on the Noometry Index (41.7 vs 42.3), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/glm-4-7-vs-qwen3-5-flash
- Last updated: 2026-10-10
- Shared benchmarks: 28

## Summary

- They share 28 benchmarks with published results for both. GLM-4.7 scores higher in 7 categories and Qwen3.5-Flash in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-4.7 leads 43.6 to 33.9.
- The biggest single-benchmark swing is Chess Puzzles: 6% for GLM-4.7 and 21% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- Qwen3.5-Flash accepts more context: 1M tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.7 | 42.3 |
| Rank | 117 | 105 |
| Context | 205K | 1M |
| Input $/M | $0.60 | $0.10 |
| Output $/M | $2.20 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.7: 43.6 (#74)
- Qwen3.5-Flash: 33.9 (#234)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1435 | 1243 |
| LMArena Coding | 1454 | 1412 |
| ALE-Bench | 399.48 | 221.8 |
| SciCode | 45.1% | — |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- Qwen3.5-Flash: —

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | 2,377 | 462.69 |
| Terminal-Bench | 33.4% | — |

## Reasoning

- GLM-4.7: 23.7 (#161)
- Qwen3.5-Flash: 32.9 (#63)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 6% | 21% |
| LMArena Hard Prompts | 1443 | 1404 |
| Epoch Capabilities Index | 143.53 | 143.98 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Mystery Game Puzzles | — | 20% |
| DTBench | — | 82.9% |
| LMCA | — | 29.1% |

## Math

- GLM-4.7: 37.7 (#92)
- Qwen3.5-Flash: 36.3 (#117)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 84.4% |
| LMArena Math | 1423 | 1407 |
| FrontierMath (Feb 2025 set) | 2.4% | 6.2% |
| FrontierMath Tier 4 (v1) | 0% | 0% |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| ProofBench | 6% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- Qwen3.5-Flash: 43.2 (#84)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 83.3% | 82.3% |
| SimpleQA Verified | 32.2% | 20.3% |
| Vectara Hallucination Rate | 11.7% | 10.5% |
| LMArena Expert | 1424 | 1408 |

## Multilingual

- GLM-4.7: 53.0 (#75)
- Qwen3.5-Flash: 50.7 (#116)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1417 | 1385 |
| LMArena Chinese | 1495 | 1446 |
| LMArena French | 1432 | 1412 |
| LMArena German | 1426 | 1389 |
| LMArena Japanese | 1439 | 1369 |
| LMArena Korean | 1401 | 1344 |
| LMArena Russian | 1423 | 1379 |
| LMArena Spanish | 1433 | 1399 |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- Qwen3.5-Flash: 72.8 (#133)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1411 | 1374 |

## Long Context

- GLM-4.7: 42.8 (#111)
- Qwen3.5-Flash: 42.4 (#119)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1432 | 1392 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- Qwen3.5-Flash: 58.2 (#117)

| Benchmark | GLM-4.7 | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1436 | 1397 |
| LMArena Creative Writing | 1401 | 1343 |
| LMArena Multi-Turn | 1446 | 1393 |
| EQ-Bench Creative Writing | 1413 | — |

## FAQ

### Is GLM-4.7 better than Qwen3.5-Flash?

GLM-4.7 and Qwen3.5-Flash score almost the same on the Noometry Index (41.7 vs 42.3), so choose on price, context window or the category you care about most.

### Which is cheaper, GLM-4.7 or Qwen3.5-Flash?

Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

### Is GLM-4.7 or Qwen3.5-Flash better for coding?

GLM-4.7 scores higher on coding benchmarks: 43.6 versus 33.9 in the Noometry coding category.

### Which has the bigger context window?

Qwen3.5-Flash does, with 1M tokens against 205K.

### How many benchmarks do GLM-4.7 and Qwen3.5-Flash share?

28 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3.5-Flash has 32.