# GLM-5.3 vs Qwen3-Coder 480B-A35B Instruct

> GLM-5.3 is the stronger model overall, scoring 54.8 to 38.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-vs-qwen3-coder-480b-a35b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 37.6.
- The biggest single-benchmark swing is WeirdML: 75.4% for GLM-5.3 and 41.2% for Qwen3-Coder 480B-A35B Instruct.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- GLM-5.3 accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 54.8 | 38.1 |
| Rank | 26 | 190 |
| Context | 1M | 262K |
| Input $/M | $1.40 | $1.50 |
| Output $/M | $4.40 | $7.50 |
| Weights | Open | Open |

## Coding

- GLM-5.3: 59.5 (#14)
- Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1622 | 1275 |
| WeirdML | 75.4% | 41.2% |
| LMArena Coding | 1496 | 1412 |
| ALE-Bench | 1,317 | 461.45 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| GSO | — | 4.9% |
| AlgoTune | — | 1.44 |

## Agentic & Tool Use

- GLM-5.3: 36.4 (#38)
- Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |

## Reasoning

- GLM-5.3: 46.1 (#46)
- Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1489 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |

## Math

- GLM-5.3: 62.3 (#33)
- Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1489 | 1365 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |

## Knowledge

- GLM-5.3: 58.3 (#37)
- Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1516 | 1338 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |

## Multilingual

- GLM-5.3: 55.7 (#28)
- Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1457 | 1346 |
| LMArena Chinese | 1528 | 1357 |
| LMArena French | 1499 | 1398 |
| LMArena German | 1499 | 1325 |
| LMArena Japanese | 1453 | 1310 |
| LMArena Korean | 1472 | 1305 |
| LMArena Russian | 1463 | 1366 |
| LMArena Spanish | 1460 | 1360 |

## Instruction Following

- GLM-5.3: 77.5 (#23)
- Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1477 | 1355 |

## Long Context

- GLM-5.3: 45.4 (#41)
- Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1482 | 1378 |

## Writing & Preference

- GLM-5.3: 75.7 (#6)
- Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)

| Benchmark | GLM-5.3 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1471 | 1357 |
| LMArena Creative Writing | 1457 | 1333 |
| LMArena Multi-Turn | 1472 | 1365 |
| EQ-Bench Creative Writing | 2075 | — |

## FAQ

### Is GLM-5.3 better than Qwen3-Coder 480B-A35B Instruct?

GLM-5.3 is the stronger model overall, scoring 54.8 to 38.1 on the Noometry Index.

### Which is cheaper, GLM-5.3 or Qwen3-Coder 480B-A35B Instruct?

GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.

### Is GLM-5.3 or Qwen3-Coder 480B-A35B Instruct better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 35.5 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.3 and Qwen3-Coder 480B-A35B Instruct share?

20 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.