# GPT-6 Astra vs Qwen3-Coder 480B-A35B Instruct

> GPT-6 Astra is the stronger model overall, scoring 70.8 to 38.1 on the Noometry Index. Qwen3-Coder 480B-A35B Instruct costs 6.7× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-6-astra-vs-qwen3-coder-480b-a35b-instruct
- Last updated: 2026-10-12
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GPT-6 Astra 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 reasoning, where GPT-6 Astra leads 85.1 to 25.5.
- The biggest single-benchmark swing is GSO: 79.4% for GPT-6 Astra and 4.9% for Qwen3-Coder 480B-A35B Instruct.
- Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 / $7.50 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 70.8 | 38.1 |
| Rank | 1 | 190 |
| Context | 1.05M | 262K |
| Input $/M | $10 | $1.50 |
| Output $/M | $50 | $7.50 |
| Weights | Proprietary | Open |

## Coding

- GPT-6 Astra: 73.7 (#2)
- Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1786 | 1275 |
| GSO | 79.4% | 4.9% |
| WeirdML | 93.6% | 41.2% |
| LMArena Coding | 1487 | 1412 |
| ALE-Bench | 2,951 | 461.45 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| FrontierSWE | 65.5% | — |
| SciCode | 56.5% | — |
| MirrorCode | 46.7% | — |
| AlgoTune | — | 1.44 |

## Agentic & Tool Use

- GPT-6 Astra: 52.9 (#3)
- Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| APEX-Agents | 64.7% | — |
| Remote Labor Index | 20.8% | — |
| BALROG | 68.3% | — |
| GDP.pdf | 34.2% | — |
| Vending-Bench 2 | 15,515 | — |

## Reasoning

- GPT-6 Astra: 85.1 (#1)
- Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1462 | 1372 |
| ARC-AGI-2 | 95% | — |
| Kagi LLM Benchmark | — | 49.5% |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 72% | — |
| EBR-Bench | 76.2% | — |
| Mystery Game Puzzles | 84% | — |
| DTBench | 97.3% | — |
| LMCA | 64.4% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 166.45 | — |

## Math

- GPT-6 Astra: 93.5 (#2)
- Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1465 | 1365 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| FrontierMath Erdős | 2.9% | — |

## Knowledge

- GPT-6 Astra: 75.3 (#1)
- Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1483 | 1338 |
| GPQA Diamond | 95.8% | — |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| Vectara Hallucination Rate | 8.7% | — |

## Multimodal

- GPT-6 Astra: 55.0 (#3)
- Qwen3-Coder 480B-A35B Instruct: —

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |

## Multilingual

- GPT-6 Astra: 53.7 (#61)
- Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1430 | 1346 |
| LMArena Chinese | 1484 | 1357 |
| LMArena French | 1456 | 1398 |
| LMArena German | 1440 | 1325 |
| LMArena Japanese | 1379 | 1310 |
| LMArena Korean | 1426 | 1305 |
| LMArena Russian | 1436 | 1366 |
| LMArena Spanish | 1407 | 1360 |

## Instruction Following

- GPT-6 Astra: 76.3 (#44)
- Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1450 | 1355 |

## Long Context

- GPT-6 Astra: 44.5 (#62)
- Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1456 | 1378 |

## Writing & Preference

- GPT-6 Astra: 75.3 (#7)
- Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)

| Benchmark | GPT-6 Astra | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1441 | 1357 |
| LMArena Creative Writing | 1418 | 1333 |
| LMArena Multi-Turn | 1448 | 1365 |
| EQ-Bench Creative Writing | 2173 | — |

## FAQ

### Is GPT-6 Astra better than Qwen3-Coder 480B-A35B Instruct?

GPT-6 Astra is the stronger model overall, scoring 70.8 to 38.1 on the Noometry Index. Qwen3-Coder 480B-A35B Instruct costs 6.7× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.

### Which is cheaper, GPT-6 Astra or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; GPT-6 Astra lists at $10 and $50.

### Is GPT-6 Astra or Qwen3-Coder 480B-A35B Instruct better for coding?

GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 35.5 in the Noometry coding category.

### Which has the bigger context window?

GPT-6 Astra does, with 1.05M tokens against 262K.

### How many benchmarks do GPT-6 Astra and Qwen3-Coder 480B-A35B Instruct share?

21 benchmarks have published results for both models. GPT-6 Astra has 56 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.