Model comparison
GPT-5.6 Sol vs Qwen3-Next 80B-A3B Instruct
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 43.0 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 9.1× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GPT-5.6 Sol scores higher in 8 categories and Qwen3-Next 80B-A3B Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 38.8.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 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-5.6 Sol | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 65.0 | 43.0 |
| Released | 2026-07-09 | 2025-09 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 33K |
| Input $ / M tokens | $4 | $0.50 |
| Output $ / M tokens | $20 | $2 |
| Results tracked | 65 | 25 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1498 | 1440 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| GSO | 76.5% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Sol: 50.3 (#7), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 67% | 66.7% |
| LMArena Hard Prompts | 1484 | 1428 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| DTBench | 96% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 161.66 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1474 | 1440 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 46.7% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Vectara Hallucination Rate | 12.4% | 9.3% |
| LMArena Expert | 1516 | 1417 |
| GPQA Diamond | 93.5% | — |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 78.6% |
| GPQA (HELM) | — | 63% |
Multimodal Not comparable
GPT-5.6 Sol: 48.6 (#9), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.3 (#32), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1452 | 1407 |
| LMArena Chinese | 1527 | 1460 |
| LMArena French | 1477 | 1413 |
| LMArena German | 1476 | 1417 |
| LMArena Japanese | 1471 | 1395 |
| LMArena Korean | 1442 | 1364 |
| LMArena Russian | 1468 | 1404 |
| LMArena Spanish | 1441 | 1435 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1482 | 1389 |
| IFEval | — | 81% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.4 (#42), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1480 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)
| Benchmark | GPT-5.6 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1457 | 1417 |
| LMArena Creative Writing | 1448 | 1334 |
| LMArena Multi-Turn | 1460 | 1416 |
| EQ-Bench Creative Writing | 1972 | — |
| WildBench | — | 80.7% |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Qwen3-Next 80B-A3B Instruct?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 43.0 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 9.1× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 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-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Qwen3-Next 80B-A3B Instruct better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 42.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-5.6 Sol and Qwen3-Next 80B-A3B Instruct share?
19 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.