Alibaba (Qwen), open weights

# Qwen3-Next 80B-A3B Instruct

> Qwen3-Next 80B-A3B Instruct by Alibaba (Qwen), released September 2025. Ranked #102 of 354 with a Noometry Index of 43.0. API: $0.50 in / $2 out per M tokens. 131K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/qwen3-next-80b-a3b-instruct
- Last updated: 2026-10-10
- Title: Qwen3-Next 80B-A3B Instruct Benchmarks, Price & Rank (October 2026)

Qwen3-Next 80B-A3B Instruct by Alibaba (Qwen) ranks 102nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 43.0. Its strongest category is reasoning, where it ranks 81st. API pricing starts at $0.50 per million input tokens and $2 per million output tokens, with a 131K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #102 of 354
- **Index score:** 43.0
- **Evidence:** Confirmed 25 results
- **Provider:** [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba)
- **Released:** September 1, 2025
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 131K
- **Max output:** 33K
- **Input price:** $0.50 / M
- **Output price:** $2 / M
- **Blended price:** $0.88 / M
- **Output speed:** 111 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #95 of 219
- **Knowledge cutoff:** April 2025
- **Input:** text
- **Hugging Face:** [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct)

## Category scores

Each category score combines every public result we have in that category.

Qwen3-Next 80B-A3B Instruct category scores

1.  Coding 42.5
2.  Reasoning 31.1
3.  Math 38.8
4.  Knowledge 41.8
5.  Multilingual 52.1
6.  Instruction Following 70.8
7.  Long Context 37.0
8.  Writing & Preference 58.0
9.  020406080

Qwen3-Next 80B-A3B Instruct category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 42.5 | #98 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 31.1 | #81 | 2 |
| [Math](https://noometry.com/best/math) | 38.8 | #126 | 2 |
| [Knowledge](https://noometry.com/best/knowledge) | 41.8 | #106 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 52.1 | #93 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 70.8 | #159 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 37.0 | #223 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 58.0 | #121 | 4 |

## Strengths and weaknesses

Categories where Qwen3-Next 80B-A3B Instruct places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Qwen3-Next 80B-A3B Instruct: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 31.1 | +7.5 | #81 of 350, top 24% |
| [Coding](https://noometry.com/best/coding) | 42.5 | +3.8 | #98 of 340, top 29% |
| [Multilingual](https://noometry.com/best/multilingual) | 52.1 | +4.7 | #93 of 297, top 32% |

### Weakest categories

Qwen3-Next 80B-A3B Instruct: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 37.0 | −3.9 | #223 of 296, top 76% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 70.8 | −0.5 | #159 of 305, top 53% |
| [Writing & Preference](https://noometry.com/best/writing) | 58.0 | +4.3 | #121 of 312, top 39% |

## Closest competitors

The models ranked just above and below Qwen3-Next 80B-A3B Instruct. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Qwen3-Next 80B-A3B Instruct
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Hunyuan Vision 1.5](https://noometry.com/models/hunyuan-vision-1-5) | #98 | 43.1 | — | — | [Compare](https://noometry.com/compare/hunyuan-vision-1-5-vs-qwen3-next-80b-a3b-instruct) |
| [Mistral Large 4](https://noometry.com/models/mistral-large-4) | #99 | 43.1 | $1.03 | — | [Compare](https://noometry.com/compare/mistral-large-4-vs-qwen3-next-80b-a3b-instruct) |
| [Claude Opus 4](https://noometry.com/models/claude-opus-4) | #100 | 43.1 | $30 | 29 | [Compare](https://noometry.com/compare/claude-opus-4-vs-qwen3-next-80b-a3b-instruct) |
| [Amazon Nova Experimental Chat 11 10](https://noometry.com/models/amazon-nova-experimental-chat-11-10) | #101 | 43.0 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-11-10-vs-qwen3-next-80b-a3b-instruct) |
| [MiMo-V2-Pro](https://noometry.com/models/mimo-v2-pro) | #103 | 43.0 | $0.54 | — | [Compare](https://noometry.com/compare/mimo-v2-pro-vs-qwen3-next-80b-a3b-instruct) |
| [Amazon Nova Experimental Chat 12 10](https://noometry.com/models/amazon-nova-experimental-chat-12-10) | #104 | 42.9 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-12-10-vs-qwen3-next-80b-a3b-instruct) |
| [o3-pro](https://noometry.com/models/o3-pro) | #105 | 42.9 | $35 | 1 | [Compare](https://noometry.com/compare/o3-pro-vs-qwen3-next-80b-a3b-instruct) |
| [Qwen3.5 Plus](https://noometry.com/models/qwen3-5-plus) | #106 | 42.9 | $0.90 | — | [Compare](https://noometry.com/compare/qwen3-5-plus-vs-qwen3-next-80b-a3b-instruct) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Qwen3-Next 80B-A3B Instruct Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1440 | #97 of 294, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1391 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Reasoning

Qwen3-Next 80B-A3B Instruct Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 66.7% | #31 of 99, top 32% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 54.4% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1428 | #93 of 297, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1371 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Math

Qwen3-Next 80B-A3B Instruct Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 46.7% | #19 of 57, top 34% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1440 | #71 of 285, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1398 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

Qwen3-Next 80B-A3B Instruct Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 78.6% | #20 of 58, top 35% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 9.3% | #48 of 96, top 50% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 63% | #20 of 57, top 36% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1417 | #110 of 273, top 41% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1376 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

Qwen3-Next 80B-A3B Instruct Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1407 | #93 of 297, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1342 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1460 | #92 of 285, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1406 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1413 | #107 of 223, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1351 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1417 | #81 of 231, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1356 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1395 | #63 of 211, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1280 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1364 | #89 of 213, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1310 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1404 | #102 of 283, top 37% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1338 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1435 | #73 of 226, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1358 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Qwen3-Next 80B-A3B Instruct Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 81% | #40 of 57, top 71% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1389 | #113 of 298, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1344 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Qwen3-Next 80B-A3B Instruct Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 41.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 55.6% | #32 of 47, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1403 | #114 of 291, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1353 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Qwen3-Next 80B-A3B Instruct Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1417 | #101 of 297, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1368 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1334 | #141 of 295, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1315 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 80.7% | #26 of 57, top 46% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1416 | #101 of 295, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1346 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Qwen3-Next 80B-A3B Instruct API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [alibaba](https://www.alibabacloud.com/help/en/model-studio/models) | $0.50 | $2 | — | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.15 | $1.20 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.09 | $1.10 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/qwen/qwen3-next-80b-a3b-instruct) | $0.10 | $1.10 | $0.07 | 2026-10-10 |

[All Alibaba (Qwen) API prices →](https://noometry.com/llm-pricing/alibaba) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Qwen3-Next 80B-A3B Instruct

-   [Qwen3-Next 80B-A3B Instruct vs Amazon Nova Experimental Chat 11 10](https://noometry.com/compare/amazon-nova-experimental-chat-11-10-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs MiMo-V2-Pro](https://noometry.com/compare/mimo-v2-pro-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Claude Opus 4](https://noometry.com/compare/claude-opus-4-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Amazon Nova Experimental Chat 12 10](https://noometry.com/compare/amazon-nova-experimental-chat-12-10-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Mistral Large 4](https://noometry.com/compare/mistral-large-4-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs o3-pro](https://noometry.com/compare/o3-pro-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs Muse Spark 1.3](https://noometry.com/compare/muse-spark-1-3-vs-qwen3-next-80b-a3b-instruct)
-   [Qwen3-Next 80B-A3B Instruct vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-qwen3-next-80b-a3b-instruct)

## Other Alibaba (Qwen) models

-   [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max)56.8
-   [Qwen3.7 Max](https://noometry.com/models/qwen3-7-max)51.5
-   [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview)51.5
-   [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus)47.5
-   [Qwen3.5 397B-A17B](https://noometry.com/models/qwen3-5-397b-a17b)46.0
-   [Qwen3.8 27B](https://noometry.com/models/qwen3-8-27b)46.0
-   [Qwen3.5 Max Preview](https://noometry.com/models/qwen3-5-max-preview)45.3
-   [Qwen3.7 Plus](https://noometry.com/models/qwen3-7-plus)45.3

## Frequently asked questions

### How good is Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct by Alibaba (Qwen) ranks 102nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 43.0. Its strongest category is reasoning, where it ranks 81st. API pricing starts at $0.50 per million input tokens and $2 per million output tokens, with a 131K-token context window.

### How much does Qwen3-Next 80B-A3B Instruct cost?

Qwen3-Next 80B-A3B Instruct costs $0.50 per million input tokens and $2 per million output tokens on Alibaba (Qwen)'s own API.

### What is Qwen3-Next 80B-A3B Instruct's context window?

Qwen3-Next 80B-A3B Instruct accepts up to 131K tokens of input and can write up to 33K tokens in one response.

### Is Qwen3-Next 80B-A3B Instruct open source?

Yes. Qwen3-Next 80B-A3B Instruct's weights are downloadable from Hugging Face (Qwen/Qwen3-Next-80B-A3B-Instruct); check the license for commercial terms.

### How fast is Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct generated about 111 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are Qwen3-Next 80B-A3B Instruct's strengths and weaknesses?

Relative to other ranked models, Qwen3-Next 80B-A3B Instruct places best in reasoning, coding, multilingual and lowest in long context, instruction following, writing & preference.

### What is Qwen3-Next 80B-A3B Instruct best at?

Its best category is reasoning, where it ranks 81st on Noometry.

### Cite this page

Noometry. (2026). Qwen3-Next 80B-A3B Instruct benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/qwen3-next-80b-a3b-instruct

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/qwen3-next-80b-a3b-instruct.md).