Model comparison
GPT-6.1 Sol vs Qwen3-Next 80B-A3B Instruct
GPT-6.1 Sol is the stronger model overall, scoring 65.6 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-6.1 Sol's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. GPT-6.1 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-6.1 Sol leads 93.7 to 38.8.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 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-6.1 Sol | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 65.6 | 43.0 |
| Released | 2026-09-29 | 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 | 34 | 25 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1487 | 1440 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| LMArena WebDev | 1755 | — |
| SciCode | 55.8% | — |
Agentic & Tool Use Not comparable
GPT-6.1 Sol: 39.6 (#26), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| APEX-Agents | 60% | — |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1466 | 1428 |
| ARC-AGI-2 | 94.2% | — |
| Kagi LLM Benchmark | — | 66.7% |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| Mystery Game Puzzles | 80% | — |
| Epoch Capabilities Index | 166.09 | — |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1464 | 1440 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 46.7% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Expert | 1502 | 1417 |
| GPQA Diamond | 95.4% | — |
| SimpleQA Verified | 73.9% | — |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1438 | 1407 |
| LMArena Chinese | 1477 | 1460 |
| LMArena Russian | 1455 | 1404 |
| LMArena French | — | 1413 |
| LMArena German | — | 1417 |
| LMArena Japanese | — | 1395 |
| LMArena Korean | — | 1364 |
| LMArena Spanish | — | 1435 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1468 | 1389 |
| IFEval | — | 81% |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1465 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)
| Benchmark | GPT-6.1 Sol | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1447 | 1417 |
| LMArena Creative Writing | 1432 | 1334 |
| LMArena Multi-Turn | 1449 | 1416 |
| WildBench | — | 80.7% |
Frequently asked questions
Is GPT-6.1 Sol better than Qwen3-Next 80B-A3B Instruct?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 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-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6.1 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-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or Qwen3-Next 80B-A3B Instruct better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 42.5 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6.1 Sol and Qwen3-Next 80B-A3B Instruct share?
12 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.