# GLM-5.3-Flash vs Qwen3.8 Max

> Qwen3.8 Max is the stronger model overall, scoring 56.0 to 49.6 on the Noometry Index. GLM-5.3-Flash costs 13× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-qwen3-8-max
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. GLM-5.3-Flash scores higher in 3 categories and Qwen3.8 Max in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 Max leads 53.4 to 31.7.
- The biggest single-benchmark swing is ProofBench: 21% for GLM-5.3-Flash and 58% for Qwen3.8 Max.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 49.6 | 56.0 |
| Rank | 45 | 21 |
| Context | 1M | 1M |
| Input $/M | $0.15 | $2 |
| Output $/M | $0.50 | $6 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Qwen3.8 Max: 52.3 (#30)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| DeepSWE | 63.4% | 57.5% |
| LMArena WebDev | 1614 | 1671 |
| SciCode | 51.6% | 52.9% |
| LMArena Coding | 1506 | 1499 |
| FrontierCode | 31.8% | — |
| FrontierSWE | — | 15.8% |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Qwen3.8 Max: 42.6 (#17)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| GDP.pdf | 14% | 23.2% |
| APEX-Agents | 52.8% | — |
| τ²-bench Banking | — | 55.1% |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Qwen3.8 Max: 53.4 (#26)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| CritPt | 15.4% | 20% |
| Chess Puzzles | 14% | 40% |
| LMArena Hard Prompts | 1490 | 1493 |
| Mystery Game Puzzles | 8% | 38% |
| Epoch Capabilities Index | 151.9 | 156.69 |
| NYT Connections (extended) | — | 88.3% |
| DTBench | — | 92% |
| LMCA | — | 46.2% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Qwen3.8 Max: 73.1 (#15)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 74.7% |
| FrontierMath Tier 4 | 17.1% | 46.3% |
| OTIS Mock AIME 2024-2025 | 93.9% | 100% |
| ProofBench | 21% | 58% |
| LMArena Math | 1499 | 1498 |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Qwen3.8 Max: 61.7 (#25)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 90.2% | 92.7% |
| LMArena Expert | 1512 | 1504 |
| SimpleQA Verified | — | 47.3% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Qwen3.8 Max: 36.9 (#70)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | 1304 | 1314 |
| Furniture Assembly | — | 20% |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Qwen3.8 Max: 56.6 (#18)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1462 | 1468 |
| LMArena Chinese | 1529 | 1537 |
| LMArena French | 1495 | 1502 |
| LMArena German | 1457 | 1489 |
| LMArena Japanese | 1420 | 1466 |
| LMArena Korean | 1443 | 1467 |
| LMArena Russian | 1472 | 1478 |
| LMArena Spanish | 1471 | 1495 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Qwen3.8 Max: 77.7 (#19)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1480 | 1478 |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Qwen3.8 Max: 45.5 (#30)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1479 | 1486 |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Qwen3.8 Max: 67.0 (#31)

| Benchmark | GLM-5.3-Flash | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1471 | 1480 |
| LMArena Creative Writing | 1440 | 1478 |
| LMArena Multi-Turn | 1471 | 1487 |

## FAQ

### Is GLM-5.3-Flash better than Qwen3.8 Max?

Qwen3.8 Max is the stronger model overall, scoring 56.0 to 49.6 on the Noometry Index. GLM-5.3-Flash costs 13× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.3-Flash or Qwen3.8 Max?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.8 Max lists at $2 and $6.

### Is GLM-5.3-Flash or Qwen3.8 Max better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 52.3 in the Noometry coding category.

### Which has the bigger context window?

Both accept 1M tokens.

### How many benchmarks do GLM-5.3-Flash and Qwen3.8 Max share?

31 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen3.8 Max has 38.