Qwen3-Next-80B-A3B Instruct is a highly advanced, instruction-tuned large language model engineered for exceptional speed, stability, and ultra-long context handling with high throughput. It achieves significant improvements in speed and cost-efficiency by activating only a small portion of its 80 billion parameters, all without compromising performance in critical areas like complex reasoning and code generation.

⚙️ Technical Specifications

Qwen3-Next-80B-A3B Instruct optimizes its operations by activating only about 3 billion parameters out of 80 billion during inference. This sparse activation mechanism provides substantial advantages:

• Speed & Cost-Efficiency: Operates approximately 10 times faster and more cost-efficiently compared to the previous Qwen3-32B model.
• Throughput: Delivers over 10 times higher throughput when processing long contexts of 32K tokens or more.
• Flexible Deployment: Offers versatile deployment options, including serverless, on-demand dedicated, and monthly reserved hosting.
• Deployment Compatibility: Compatible with SGLang and vLLM for efficient, scalable usage, featuring advanced multi-token prediction capabilities.

📊 Performance Benchmarks

• ✅ Top-Tier Performance: Matches or closely approximates the performance of the Qwen3-235B flagship model across various reasoning, code completion, and instruction-following tasks.
• ✅ Unwavering Long-Context Handling: Consistently provides stable and deterministic answers, particularly excelling in tasks demanding extensive context understanding.
• ✅ Resource-Efficient: Surpasses earlier mid-sized instruction-tuned models in efficiency, achieving high performance with fewer computational resources.
• ✅ Versatile Integration: Highly suitable for tools integration, Retrieval-Augmented Generation (RAG), and sophisticated agentic workflows that require consistent chain-of-thought outputs.

💰 API Pricing

✨ Key Capabilities

• 🚀 Ultra-Efficient Inference: Employs a sparse Mixture-of-Experts (MoE) architecture, dynamically activating only 3 billion parameters out of 80 billion for significantly faster and more cost-effective inference.
• 🧠 Exceptional Task Performance: Excels in a broad range of complex tasks, including advanced reasoning, robust code generation, precise knowledge question answering, and versatile multilingual applications.
• ⚡️ Stable & Rapid Responses: Optimized for instruction mode, ensuring quick and consistent responses without intermediate "thinking" steps.
• 📖 Ultra-Long Context Handling: Features a native context length of 262K tokens, extendable up to an impressive 1 million tokens using advanced scaling technology.
• 📈 High Throughput: Achieves a 10x improvement in throughput for processing extensive long contexts compared to previous models.
• 💬 Consistent Dialogue & Answers: Ideal for multi-turn dialogues and tasks that demand deterministic, consistent final answers.
• 🛠️ Advanced Agentic Workflows: Strong capabilities for tool calling, multi-step task execution, and sophisticated agentic workflows with seamlessly integrated tools.

💡 Use Cases

• Code Generation: Accelerate software development through intelligent code suggestions and full code block generation.
• Content Creation & Editing: Generate diverse content, from articles to marketing copy, and perform intricate editing based on detailed instructions.
• Data Analysis: Facilitate complex data interpretation, statistical analysis, and comprehensive report generation.
• Customer Service Automation: Enhance customer support efficiency with precise instruction handling and automated responses.
• Technical Documentation: Streamline the creation of technical documents, manuals, and format-specific outputs.
• Process Automation: Execute multi-step tasks and integrate tool calling to automate and streamline various workflows.
• Long Conversation & Document Handling: Efficiently manage extended dialogues, summarize large documents, and extract key information from extensive texts.

💻 Code Sample

import openai

client = openai.OpenAI(
    base_url="https://api.perplexity.ai", # Example base URL, replace with actual endpoint
    api_key="YOUR_API_KEY", # Replace with your actual API key
)

messages = [
    {
        "role": "system",
        "content": "You are Qwen3-Next-80B-A3B Instruct, a helpful AI assistant."
    },
    {
        "role": "user",
        "content": "Explain the concept of quantum entanglement in simple terms for a high school student."
    },
]

response = client.chat.completions.create(
    model="alibaba/qwen3-next-80b-a3b-instruct",
    messages=messages,
    max_tokens=500,
    temperature=0.7,
    top_p=0.9,
    frequency_penalty=0,
    presence_penalty=0,
)

print(response.choices[0].message.content)

            

Note: The `base_url` and `api_key` in the example above are placeholders. Please refer to the official API documentation for specific integration details.

🆚 Comparison with Other Models

❓ Frequently Asked Questions