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.
Evaluations
10
across 10 benchmarks
Latency
526ms
Context length
262k
tokens
Cost
$0.10 · $0.80
input · output per 1M tokens
Key takeaways
Qwen3 Next 80B A3B Instruct is an 80B MoE instruction-tuned chat model optimized for fast, stable, and deterministic responses in complex tasks (reasoning, code generation, knowledge QA, multilingual) for production settings.
The model uses scaling-efficient training and decoding to achieve high throughput and stability on long inputs and multi-turn dialogues, prioritizing consistent final answers over showing thought processes.
The model has a high proficiency in solving diverse mathematical problems (93.6% accuracy on MATH-500) and accurate implementation of simple Python functions (63.23% accuracy on MBPP Plus).
The model struggles significantly with complex medical reasoning (6.58% accuracy on Humanity's Last Exam), showing frequent hallucination, overconfidence in incorrect answers, and difficulty with multi-step calculations in a chain.
Key failure modes include misinterpretation of complex logic, factual retrieval errors, inability to adhere to all prompt requirements, and overconfidence in incorrect answers, particularly in domains requiring deep reasoning.
