Model comparison
GPT-6 Luna vs Qwen3-Coder 480B-A35B Instruct
GPT-6 Luna is the stronger model overall, scoring 45.1 to 37.7 on the Noometry Index.
Last verified . 14 shared benchmarks.
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. GPT-6 Luna scores higher in 8 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where GPT-6 Luna leads 54.7 to 36.2.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
- GPT-6 Luna accepts more context: 1.05M tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 45.1 | 37.7 |
| Released | 2026-09-22 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.10 | $1.50 |
| Output $ / M tokens | $0.50 | $7.50 |
| Results tracked | 18 | 25 |
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Category by category
Coding GPT-6 Luna leads
GPT-6 Luna: 54.7 (#24), Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1583 | 1275 |
| LMArena Coding | 1448 | 1413 |
| DeepSWE | 66.6% | — |
| FrontierCode | 42.4% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| GSO | — | 4.9% |
| WeirdML | — | 41.2% |
| ALE-Bench | — | 461.45 |
| AlgoTune | — | 1.44 |
Agentic & Tool Use Not comparable
GPT-6 Luna: —, Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
Reasoning GPT-6 Luna leads
GPT-6 Luna: 31.7 (#68), Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1415 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| NYT Connections (extended) | 68.7% | — |
Math Too close to call
GPT-6 Luna: 35.6 (#140), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1396 | 1364 |
Knowledge GPT-6 Luna leads
GPT-6 Luna: 40.1 (#110), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1458 | 1337 |
Multimodal Not comparable
GPT-6 Luna: 42.0 (#27), Qwen3-Coder 480B-A35B Instruct: —
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Furniture Assembly | 44.2% | — |
Multilingual GPT-6 Luna leads
GPT-6 Luna: 51.0 (#111), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1390 | 1345 |
| LMArena Chinese | 1425 | 1356 |
| LMArena Russian | 1392 | 1367 |
| LMArena Spanish | 1416 | 1360 |
| LMArena French | — | 1394 |
| LMArena German | — | 1324 |
| LMArena Japanese | — | 1309 |
| LMArena Korean | — | 1307 |
Instruction Following GPT-6 Luna leads
GPT-6 Luna: 74.7 (#85), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1414 | 1356 |
Long Context GPT-6 Luna leads
GPT-6 Luna: 43.3 (#95), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1417 | 1379 |
Writing & Preference GPT-6 Luna leads
GPT-6 Luna: 58.4 (#115), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | GPT-6 Luna | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1392 | 1356 |
| LMArena Creative Writing | 1357 | 1333 |
| LMArena Multi-Turn | 1400 | 1365 |
Frequently asked questions
Is GPT-6 Luna better than Qwen3-Coder 480B-A35B Instruct?
GPT-6 Luna is the stronger model overall, scoring 45.1 to 37.7 on the Noometry Index.
Which is cheaper, GPT-6 Luna or Qwen3-Coder 480B-A35B Instruct?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is GPT-6 Luna or Qwen3-Coder 480B-A35B Instruct better for coding?
GPT-6 Luna scores higher on coding benchmarks: 54.7 versus 36.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 262K.
How many benchmarks do GPT-6 Luna and Qwen3-Coder 480B-A35B Instruct share?
14 benchmarks have published results for both models. GPT-6 Luna has 18 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.