Qwen3.5

阿里巴巴

A lightweight, 0.8 billion parameter model from the Qwen3.5 series, optimized for fast inference and low-cost deployment. It is designed for simple, non-reasoning tasks and is suitable for edge devices or applications requiring rapid response times.

阿里巴巴

Qwen3.5 0.8B (Reasoning) is a lightweight, 0.8-billion parameter model from Alibaba's Qwen3.5 series, specifically optimized for reasoning tasks. It is designed to deliver strong logical and analytical performance while maintaining a small footprint suitable for edge deployment or low-latency applications.

阿里巴巴

A large language model from Alibaba's Qwen3.5 series, featuring 122B total parameters with 10B activated (A10B). This non-reasoning variant is optimized for high efficiency and low latency in general-purpose tasks, offering strong multilingual and multimodal capabilities without the overhead of complex reasoning chains.

阿里巴巴

Qwen3.5 122B A10B is a large-scale Mixture-of-Experts (MoE) model from Alibaba's Qwen series, optimized for complex reasoning tasks. It features a 122 billion parameter architecture with 10 billion active parameters, balancing high performance with computational efficiency. The model supports an extremely long context window and excels in code generation and logical analysis.

阿里巴巴

Qwen3.5 27B (Non-reasoning) is a mid-sized language model from Alibaba's Qwen series, optimized for general-purpose tasks without specialized reasoning capabilities. It supports long contexts and is designed for efficient deployment.

阿里巴巴

Qwen3.5 27B (Reasoning) is a 27-billion parameter language model from Alibaba, optimized for reasoning tasks with enhanced chain-of-thought capabilities. It is designed for complex problem-solving and logical inference.

阿里巴巴

A lightweight, 2-billion parameter model from the Qwen3.5 series optimized for fast and cost-effective inference. It is designed for general-purpose conversational tasks and simple applications where low latency and high throughput are prioritized over complex reasoning.

阿里巴巴

Qwen3.5 2B (Reasoning) is a lightweight, 2-billion parameter model from Alibaba's Qwen series, specifically optimized for reasoning tasks. It delivers efficient and fast inference while maintaining strong performance on logical and analytical problems.

阿里巴巴

This is a high-efficiency Mixture-of-Experts (MoE) model from the Qwen3.5 series, featuring 35 billion total parameters with only 3 billion activated per inference. It is optimized for fast response speeds and is the non-reasoning variant, suitable for general-purpose tasks.

阿里巴巴

A 35B parameter reasoning model from the Qwen3.5 series, utilizing a Mixture-of-Experts architecture with 3B activated parameters for efficient inference. Optimized for complex reasoning tasks while maintaining strong performance in coding and long-context understanding.

阿里巴巴

Qwen3.5 397B A17B (Non-reasoning) is a large-scale Mixture-of-Experts (MoE) model from Alibaba's Qwen series. As a non-reasoning variant, it is optimized for general-purpose tasks, offering high performance and efficiency without the overhead of extended chain-of-thought processes. It is well-suited for applications requiring fast and capable responses across coding, instruction following, and general knowledge tasks.

阿里巴巴

Qwen3.5 397B A17B is a large-scale reasoning model from Alibaba's Qwen series, featuring 397 billion total parameters with 17 billion active parameters. It is optimized for complex reasoning tasks, multi-step problem solving, and supports a wide range of languages including Chinese and English.

阿里巴巴

A lightweight, 4-billion parameter model from the Qwen3.5 series, optimized for efficiency and speed. It is designed for fast inference and low-cost deployment, suitable for edge devices and applications requiring quick responses.

阿里巴巴

Qwen3.5 4B (Reasoning) is a lightweight, 4-billion parameter model from Alibaba's Qwen3.5 series, specifically optimized for enhanced reasoning and chain-of-thought capabilities. It offers a strong balance of performance and efficiency, making it suitable for fast inference tasks that require logical deduction.

阿里巴巴

Qwen3.5 9B (Non-reasoning) is a compact language model from Alibaba's Qwen series, optimized for fast inference and low-cost deployment. It supports coding and multilingual tasks with a 9B parameter size, making it suitable for edge devices and real-time applications.

阿里巴巴

Qwen3.5 9B (Reasoning) is a lightweight, efficient model from Alibaba's Qwen series, specifically optimized for enhanced reasoning and chain-of-thought capabilities. It offers a strong balance of performance, speed, and cost-effectiveness for complex problem-solving tasks.

阿里巴巴

Qwen3.5 Omni Flash is a multimodal model from Alibaba's Qwen series, designed for fast and efficient processing of text, images, and potentially other modalities. It is optimized for low-latency applications, making it suitable for real-time interactive scenarios.

阿里巴巴

Qwen3.5 Omni Plus is a multimodal large language model from Alibaba's Qwen series, designed for enhanced performance across text, image, and potentially audio inputs. It features strong reasoning and coding capabilities, suitable for complex tasks requiring integrated understanding of different data types.

阿里巴巴

Alibaba's Qwen3.5 Plus multimodal model supporting text, image, and video inputs with 1M-token context window for reasoning and agent workflows.