LangMart: Qwen: Qwen3 Next 80B A3B Instruct

Model Overview

Description

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.

Description

LangMart: Qwen: Qwen3 Next 80B A3B Instruct is a language model provided by qwen. This model offers advanced capabilities for natural language processing tasks.

Provider

qwen

Specifications

Pricing

Capabilities

• Frequency penalty
• Logit bias
• Max tokens
• Min p
• Presence penalty
• Repetition penalty
• Response format
• Seed
• Stop
• Structured outputs
• Temperature
• Tool choice
• Tools
• Top k
• Top p

Detailed Analysis

Qwen3-Next-80B-A3B-Instruct is the first model in the next-generation Qwen3-Next series, featuring revolutionary hybrid architecture for extreme efficiency. Released September 2025. Key characteristics: (1) Architecture: 80B total parameters with only 3.9B activated per forward pass (95%+ parameter sparsity); introduces Hybrid Attention combining Gated DeltaNet and Gated Attention for efficient ultra-long context modeling; uses High-Sparsity MoE with extreme low activation ratio; Multi-Token Prediction (MTP) boosts performance and accelerates inference; (2) Performance: Outperforms Qwen3-32B-Base on downstream tasks with 10% of training cost and 10x inference throughput for contexts over 32K tokens; competitive quality with 80B-scale capacity at 3.9B inference cost; (3) Context Window: 262,144 tokens (262K) - among the longest context windows available, enabled by hybrid attention architecture; (4) Language Support: 119 languages covering 98%+ of internet users; (5) Use Cases: Ultra-long document processing, large-scale repository analysis, book-length content generation, applications requiring massive context at low inference cost, high-throughput production deployments; (6) Trade-offs: Cutting-edge hybrid architecture may have different characteristics vs traditional transformers; represents future direction of efficient AI. Best for applications requiring massive context windows or maximum throughput, demonstrating next-generation efficiency through hybrid attention and extreme sparsity.