# GLM-5.3-Flash vs Qwen1.5-72B

> GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 30.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-qwen1-5-72b
- Last updated: 2026-10-12
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen1.5-72B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 11.5.
- The biggest single-benchmark swing is GPQA Diamond: 90.2% for GLM-5.3-Flash and 28.8% for Qwen1.5-72B.

## Snapshot

| | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 30.8 |
| Rank | 41 | 285 |
| Context | 1M | — |
| Input $/M | $0.15 | — |
| Output $/M | $0.50 | — |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.1 (#31)
- Qwen1.5-72B: 31.9 (#277)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Coding | 1508 | 1165 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| BigCodeBench Instruct | — | 33.2% |
| BigCodeBench Complete | — | 40.3% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 59.1% |
| MBPP+ | — | 61.6% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.2 (#47)
- Qwen1.5-72B: —

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 48.0 (#42)
- Qwen1.5-72B: 22.2 (#203)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1148 |
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.88 | — |

## Math

- GLM-5.3-Flash: 53.3 (#47)
- Qwen1.5-72B: 33.2 (#205)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Math | 1500 | 1164 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |

## Knowledge

- GLM-5.3-Flash: 58.4 (#36)
- Qwen1.5-72B: 11.5 (#300)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| GPQA Diamond | 90.2% | 28.8% |
| LMArena Expert | 1513 | 1136 |

## Multimodal

- GLM-5.3-Flash: 42.8 (#27)
- Qwen1.5-72B: —

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Vision | 1296 | — |

## Multilingual

- GLM-5.3-Flash: 56.0 (#25)
- Qwen1.5-72B: 33.2 (#253)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Non-English | 1462 | 1135 |
| LMArena Chinese | 1527 | 1186 |
| LMArena French | 1496 | 1159 |
| LMArena German | 1470 | 1084 |
| LMArena Japanese | 1429 | 1061 |
| LMArena Korean | 1446 | 1050 |
| LMArena Russian | 1469 | 1104 |
| LMArena Spanish | 1471 | 1110 |

## Instruction Following

- GLM-5.3-Flash: 77.5 (#20)
- Qwen1.5-72B: 59.3 (#256)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Instruction Following | 1478 | 1141 |

## Long Context

- GLM-5.3-Flash: 45.4 (#39)
- Qwen1.5-72B: 35.1 (#243)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Longer Query | 1482 | 1157 |

## Writing & Preference

- GLM-5.3-Flash: 65.3 (#50)
- Qwen1.5-72B: 37.3 (#258)

| Benchmark | GLM-5.3-Flash | Qwen1.5-72B |
|---|---|---|
| LMArena Text | 1471 | 1166 |
| LMArena Creative Writing | 1442 | 1137 |
| LMArena Multi-Turn | 1467 | 1160 |

## FAQ

### Is GLM-5.3-Flash better than Qwen1.5-72B?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 30.8 on the Noometry Index.

### Is GLM-5.3-Flash or Qwen1.5-72B better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 31.9 in the Noometry coding category.

### How many benchmarks do GLM-5.3-Flash and Qwen1.5-72B share?

18 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen1.5-72B has 22.