Qwen3 Next 80B A3B Instruct
Qwen | 262,144 token context | $0.09/M input, $1.10/M output. Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final…Qwen | 262,144 token context | $0.09/M input, $1.10/M output. Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought. The model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.

Captain's notes
Known for
- Only 3B parameters activated per token; claimed 10x throughput over Qwen3-32B at long contexts.
- Reasoning scores close to Qwen3-235B-A22B on several benchmarks despite far lower compute.
- Native 262K-token context, extensible further, with stability optimizations for ultra-long inputs.
- Tops its comparison table on LiveCodeBench v6 and LiveBench, including against the 235B model.
- No thinking-mode output; clean final answers only, suited to RAG and agentic pipelines.
Watch out for
- No thinking mode at all — if you need visible chain-of-thought, this variant cannot provide it.
- 80B total weights means local deployment requires serious hardware despite the low activation count.
- Output is priced at $1.10/M tokens via API, eleven times the input rate — watch output length in production.
Prompts to try
Compare with
- Qwen3.7 FlashAPI-only model1,000,000 token context · $0.03/M input, $0.13/M output
- Qwen3.8 Max (0902)API-only model1,000,000 token context · $2.00/M input, $6.00/M output
- Qwen3.8 Omni FlashAPI-only model1,000,000 token context · $0.15/M input, $0.47/M output
- Qwen3.7 PlusAPI-only model1,000,000 token context · $0.32/M input, $1.28/M output
Questions people ask
- Can I run Qwen3-Next-80B-A3B-Instruct locally?
- The weights are open and available on Hugging Face under the Qwen organisation, so yes — but the total parameter count is 80B, which demands substantial GPU memory even with quantisation; check the VRAM guide before committing.
- What does it cost and how long is the context?
- Via API it is $0.10 per million input tokens and $1.10 per million output tokens, with a native context of 262,144 tokens.
- How does this differ from Qwen3-32B or Qwen3-235B?
- Qwen3-Next-80B-A3B activates only 3B parameters per token through a high-sparsity MoE design, giving it much faster inference than Qwen3-32B at long contexts while matching Qwen3-235B-A22B on several benchmarks; unlike other Qwen3 instruct variants it has no thinking mode.
Captain's verdictA genuinely efficient architecture that punches well above its active-parameter weight — the no-thinking-traces design makes it a practical production choice rather than a research curiosity.
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
