Model comparison
GPT-6 Sol vs Qwen3-Next 80B-A3B Instruct
GPT-6 Sol is the stronger model overall, scoring 61.8 to 43.0 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 4.6× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. GPT-6 Sol scores higher in 7 categories and Qwen3-Next 80B-A3B Instruct in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 38.8.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 131K.
- Qwen3-Next 80B-A3B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 61.8 | 43.0 |
| Released | 2026-09-22 | 2025-09 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 33K |
| Input $ / M tokens | $2 | $0.50 |
| Output $ / M tokens | $10 | $2 |
| Results tracked | 45 | 25 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1447 | 1440 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| ALE-Bench | 2,462 | — |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1428 |
| ARC-AGI-2 | 89.6% | — |
| Kagi LLM Benchmark | — | 66.7% |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| DTBench | 97.3% | — |
| LMCA | 59.1% | — |
| Epoch Capabilities Index | 162.72 | — |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1402 | 1440 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 46.7% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Vectara Hallucination Rate | 6.5% | 9.3% |
| LMArena Expert | 1439 | 1417 |
| GPQA Diamond | 94.3% | — |
| SimpleQA Verified | 60.7% | — |
| MMLU-Pro | — | 78.6% |
| GPQA (HELM) | — | 63% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual Qwen3-Next 80B-A3B Instruct leads
GPT-6 Sol: 50.5 (#118), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1385 | 1407 |
| LMArena Chinese | 1405 | 1460 |
| LMArena French | 1410 | 1413 |
| LMArena German | 1390 | 1417 |
| LMArena Japanese | 1385 | 1395 |
| LMArena Korean | 1341 | 1364 |
| LMArena Russian | 1401 | 1404 |
| LMArena Spanish | 1384 | 1435 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1412 | 1389 |
| IFEval | — | 81% |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1411 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)
| Benchmark | GPT-6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1395 | 1417 |
| LMArena Creative Writing | 1378 | 1334 |
| LMArena Multi-Turn | 1412 | 1416 |
| EQ-Bench Creative Writing | 2125 | — |
| WildBench | — | 80.7% |
Frequently asked questions
Is GPT-6 Sol better than Qwen3-Next 80B-A3B Instruct?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 43.0 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 4.6× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Sol or Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is cheaper. It lists at $0.50 per million input tokens and $2 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Qwen3-Next 80B-A3B Instruct better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 42.5 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6 Sol and Qwen3-Next 80B-A3B Instruct share?
18 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.