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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.

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Qwen3-Next-80B-A3B-Instruct is a Qwen model from Alibaba that runs 80 billion total parameters but activates only 3 billion per token, giving it throughput closer to a small model than a large one. It skips chain-of-thought entirely and targets stable, instruction-following outputs across coding, reasoning, long-context tasks, and multilingual use. If you need a capable production assistant that won't flood you with thinking traces and handles 256K-token inputs without falling apart, this is worth a look.

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

Refactor a tangled function
Here is a Python function that has grown messy over time. Refactor it so it is readable, handles edge cases explicitly, and includes a short docstring. Do not change the external interface. Paste your function below and I will return the cleaned version with a brief explanation of each change.
Plan a multi-step agent
I want to build an agent that monitors a folder for new CSV files, validates each file against a schema, writes passing rows to a Postgres table, and emails a summary of failures. Outline the tool calls the agent needs, the order it should call them, and where it should handle retries. Keep the plan concrete and implementation-ready.
Reason through a logic puzzle
Five engineers each own a different programming language as their favourite: Python, Rust, Go, TypeScript, Java. Use these clues to determine who favours what: The Rust fan sits directly left of the Go fan. The Python fan is at one end. The TypeScript fan is not next to the Python fan. The Java fan is between the Go fan and the TypeScript fan. Show your reasoning step by step.

Compare with

  • Qwen3.7 Flash
    API-only model
    1,000,000 token context · $0.03/M input, $0.13/M output
  • Qwen3.8 Max (0902)
    API-only model
    1,000,000 token context · $2.00/M input, $6.00/M output
  • Qwen3.8 Omni Flash
    API-only model
    1,000,000 token context · $0.15/M input, $0.47/M output
  • Qwen3.7 Plus
    API-only model
    1,000,000 token context · $0.32/M input, $1.28/M output
More text models from qwen

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 verdict
A 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.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
Qwen3 Next 80B A3B Instruct on ModelTrends.ai: Heat 5,419°
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