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. It's best used as a general assistant, code helper, and long-context task solver where consistent final answers are preferred.
The model employs scaling-efficient training and decoding to achieve high throughput and stability on ultra-long inputs and multi-turn dialogues. It prioritizes consistent final answers over showing thought processes, making it well-suited for RAG, tool use, and agentic workflows.
The model demonstrates high proficiency in solving diverse mathematical problems, achieving 93.6% accuracy on MATH-500, with strong performance in algebra, counting & probability, geometry, number theory, and precalculus. It also accurately implements simple Python functions (63.23% accuracy on MBPP Plus).
A significant limitation is its poor performance in complex medical reasoning, scoring only 6.58% accuracy on Humanity's Last Exam. Key failure modes include frequent hallucination, overconfidence in incorrect answers, difficulty with multi-step calculations in a chain, and misinterpretation of complex logic.
Common failure modes include misinterpretation of complex logic, factual retrieval errors, inability to adhere to all prompt requirements, and overconfidence in incorrect answers, especially in domains requiring deep reasoning or precise multi-step calculations.
