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Qwen3 Next 80B A3B Instruct

qwen3-next-80b-a3b-instruct

A new generation of open-source, non-thinking mode model powered by Qwen3. This version demonstrates superior Chinese text understanding, augmented logical reasoning, and enhanced capabilities in text generation tasks over the previous iteration (Qwen3-235B-A22B-Instruct-2507).

Available at 14 Providers

Provider Source Input Price ($/1M) Output Price ($/1M) Description Free
vercel vercel Input: $0.09 Output: $1.10 A new generation of open-source, non-thinking mode model powered by Qwen3. This version demonstrates superior Chinese text understanding, augmented logical reasoning, and enhanced capabilities in text generation tasks over the previous iteration (Qwen3-235B-A22B-Instruct-2507).
together together Input: $0.15 Output: $1.50 -
alibaba models-dev Input: $0.50 Output: $2.00 Provider: Alibaba, Context: 131072, Output Limit: 32768
nvidia models-dev Input: $0.00 Output: $0.00 Provider: Nvidia, Context: 262144, Output Limit: 16384
alibabacn models-dev Input: $0.14 Output: $0.57 Provider: Alibaba (China), Context: 131072, Output Limit: 32768
siliconflowcn models-dev Input: $0.14 Output: $1.40 Provider: SiliconFlow (China), Context: 262000, Output Limit: 262000
chutes models-dev Input: $0.10 Output: $0.80 Provider: Chutes, Context: 262144, Output Limit: 262144
siliconflow models-dev Input: $0.14 Output: $1.40 Provider: SiliconFlow, Context: 262000, Output Limit: 262000
helicone models-dev Input: $0.14 Output: $1.40 Provider: Helicone, Context: 262000, Output Limit: 16384
huggingface models-dev Input: $0.25 Output: $1.00 Provider: Hugging Face, Context: 262144, Output Limit: 66536
ionet models-dev Input: $0.10 Output: $0.80 Provider: IO.NET, Context: 262144, Output Limit: 4096
deepinfra litellm Input: $0.14 Output: $1.40 Source: deepinfra, Context: 262144
fireworksai litellm Input: $0.90 Output: $0.90 Source: fireworks_ai, Context: 4096
openrouter openrouter Input: $0.06 Output: $0.60 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. Context: 262144