Qwen3-Next-80B-A3B Instruct

Qwen3-Next-80B-A3B Instruct is a highly efficient instruction-tuned large language model designed for fast, stable responses with ultra-long context handling and high throughput. It activates only a small portion of its 80 billion parameters to achieve significant improvements in speed and cost-efficiency without sacrificing performance in reasoning, code generation, and other complex tasks.

Technical Specifications

Qwen3-Next-80B-A3B Instruct activates only about 3 billion parameters out of 80 billion during inference, making it much faster and cheaper to run — about 10 times faster and more cost-efficient compared to the earlier Qwen3-32B model. It delivers over 10 times higher throughput on long contexts of 32K tokens or more. The model supports flexible deployment options including serverless, on-demand dedicated, and monthly reserved hosting. It is compatible with SGLang and vLLM for deployment with advanced mult-token prediction capabilities, ensuring efficient and scalable usage.

Performance Benchmarks

• Performance matches or closely approaches Qwen3-235B flagship in many reasoning, code completion, and instruction-following tasks
• Excels in long-context tasks with stable, deterministic answers
• Outperforms earlier mid-sized instruction-tuned models, demonstrating efficiency with reduced computational resources
• Suitable for tools integration, retrieval-augmented generation (RAG), and agentic workflows requiring consistent chain-of-thought outputs

API Pricing

Input: $0.1575

Output: $1.6

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Key Capabilities

• Highly efficient instruction-following with sparse Mixture-of-Experts (MoE) architecture activating only 3B parameters out of 80B, offering faster and cheaper inference.
• Exceptional performance on complex tasks including reasoning, code generation, knowledge question answering, and multilingual usage.
• Stable and fast responses optimized for instruction mode without intermediate “thinking” steps.
• Supports ultra-long context handling with native 262K token length, extendable to 1 million tokens with scaling technology.
• High throughput for processing long contexts (10x improvement over previous models).
• Excellent for multi-turn dialogues and tasks requiring deterministic, consistent final answers.
• Strong capabilities for tool calling, multi-step task execution, and agentic workflows with integrated tools.

Use Cases

• Code generation and software development assistance
• Content creation and editing based on detailed instructions
• Data analysis and complex report generation
• Customer service automation with precise instruction handling
• Technical documentation generation and format-specific outputs
• Process automation including multi-step task execution and tool calling
• Handling of long conversations and large documents

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Code Sample

Comparison with Other Models

vs Qwen3-235B: The 80B A3B model matches or closely approaches the flagship 235B in reasoning and code tasks but is much more efficient, activating fewer parameters for faster, cheaper inference.

vs GPT-4.1: Qwen3-Next offers comparable instruction-following and long-context capabilities, with an edge in throughput and token window size, making it suitable for extensive document comprehension.

vs Claude 4.1 Opus: Qwen3-Next provides superior performance in multi-turn dialogues and agentic workflows, with more deterministic outputs on very long contexts compared to Claude’s conversational strengths.

vs Gemini 2.5 Flash: Qwen3-Next shows better scaling in ultra-long context handling and multi-token prediction efficiency, giving it an advantage in processing complex, multi-step reasoning tasks.