Qwen3

阿里巴巴

Qwen3 0.6B is a lightweight, non-reasoning variant of the Qwen3 series with only 0.6 billion parameters. It is optimized for fast inference, low latency, and minimal resource consumption, making it suitable for edge deployment, simple conversational tasks, and applications requiring rapid response times.

阿里巴巴

A lightweight reasoning model from the Qwen3 series, optimized for fast inference and cost-effective deployment. It excels in logical reasoning tasks with a focus on chain-of-thought capabilities.

阿里巴巴

Qwen3 1.7B is a lightweight language model from Alibaba's Qwen series, optimized for fast and efficient inference. It is designed for non-reasoning tasks, providing quick responses with minimal computational resources.

阿里巴巴

A compact 1.7B parameter model from Alibaba's Qwen3 series, optimized for efficient reasoning tasks. It is designed to deliver strong logical and analytical performance in resource-constrained environments, offering a balance of speed and capability.

阿里巴巴

Qwen3 14B is a 14-billion parameter model from Alibaba's Qwen3 series, optimized for general-purpose dialogue and instruction following. As a non-reasoning variant, it focuses on efficient and responsive text generation, making it suitable for applications requiring quick, cost-effective, and high-quality conversational AI.

阿里巴巴

Qwen3 14B (Reasoning) is a 14-billion parameter model from Alibaba's Qwen3 series, specifically optimized for complex reasoning tasks. It excels at chain-of-thought and step-by-step logical problem-solving, offering a strong balance between advanced reasoning capabilities and computational efficiency.

阿里巴巴

Qwen3 235B A22B is a large-scale Mixture-of-Experts (MoE) language model from Alibaba's Qwen series, with a total of 235 billion parameters but only 22 billion activated per inference. This non-reasoning variant is optimized for general-purpose tasks, offering strong multilingual capabilities, coding proficiency, and efficient performance due to its MoE architecture.

阿里巴巴

Qwen3 235B A22B (Reasoning) is a large-scale language model from Alibaba's Qwen3 series, optimized for complex reasoning tasks. It utilizes a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters, balancing high performance with computational efficiency. The model excels in instruction following and multi-step logical reasoning.

阿里巴巴

This is a reasoning-optimized variant of the Qwen3 235B model from Alibaba Cloud. It is designed to excel in complex logical, mathematical, and coding tasks that require multi-step reasoning. As a large-scale model, it supports long context windows and is part of the advanced Qwen3 series.

阿里巴巴

Qwen3 235B A22B is a large-scale Mixture-of-Experts (MoE) language model from Alibaba's Qwen series. It features 235 billion total parameters with 22 billion activated per token, designed for strong instruction following, complex reasoning, and multilingual tasks.

阿里巴巴

Qwen3 30B A3B is a 30-billion parameter model from Alibaba's Qwen3 series, optimized for general-purpose instruction following and fast response generation. As a non-reasoning variant, it prioritizes efficiency and speed over complex chain-of-thought tasks, making it suitable for cost-sensitive and latency-critical applications.

阿里巴巴

Qwen3 30B A3B is a reasoning-optimized language model from Alibaba, designed for enhanced logical inference and problem-solving tasks.

阿里巴巴

This is a 30-billion parameter reasoning model from Alibaba's Qwen3 series, optimized for complex logical and analytical tasks. It features enhanced chain-of-thought capabilities to improve accuracy in multi-step problem-solving.

阿里巴巴

Qwen3 30B A3B is a 30-billion parameter instruction-tuned model from Alibaba's Qwen3 series, likely utilizing a Mixture-of-Experts architecture with 3 billion active parameters. It is optimized for strong instruction following, reasoning, and multilingual (especially Chinese) performance, balancing capability with inference efficiency.

阿里巴巴

Qwen3 32B (Non-reasoning) is a 32-billion parameter instruction-tuned model from Alibaba's Qwen series. It is designed for general-purpose dialogue and content generation, balancing performance and efficiency. This model excels at following instructions and handling a wide range of tasks without specialized reasoning modes.

阿里巴巴

Qwen3 32B (Reasoning) is a 32-billion parameter model from Alibaba's Qwen3 series, specifically optimized for complex reasoning tasks. It excels in chain-of-thought processes, logical deduction, and problem-solving, while also maintaining strong coding and long-context capabilities.

阿里巴巴

Qwen3 4B (Non-reasoning) is a lightweight, 4-billion parameter language model from Alibaba's Qwen3 series, optimized for fast and cost-effective inference. It is designed for general-purpose tasks and edge deployment, offering a balance of performance and efficiency without the overhead of complex reasoning chains.

阿里巴巴

Qwen3 4B (Reasoning) is a compact 4-billion parameter model from Alibaba's Qwen3 series, optimized for reasoning tasks. It likely incorporates a chain-of-thought or thinking mode to enhance logical problem-solving while maintaining low latency and cost. This model is suitable for deployment in resource-constrained environments requiring efficient reasoning capabilities.

阿里巴巴

A lightweight 4B-parameter reasoning model from Alibaba's Qwen3 series, optimized for instruction following and logical reasoning tasks. It offers a balance of performance and efficiency for resource-constrained deployments.

阿里巴巴

Qwen3 4B is a lightweight, efficient instruction-tuned model from Alibaba's Qwen series. It is optimized for fast inference and low-cost deployment while maintaining strong performance in following instructions and general tasks, particularly for Chinese language processing.

阿里巴巴

Qwen3 8B (Non-reasoning) is an 8-billion parameter instruction-tuned model from Alibaba's Qwen3 series, optimized for general-purpose dialogue and instruction-following tasks. It is designed for fast response speeds and cost-effective deployment, making it suitable for applications requiring efficient and capable language understanding without complex reasoning chains.

阿里巴巴

Qwen3 8B (Reasoning) is a lightweight, 8-billion parameter model from Alibaba's Qwen3 series, optimized for instruction following and reasoning tasks. It delivers strong logical and analytical performance while maintaining fast inference speeds suitable for real-time applications.

阿里巴巴

Qwen3 Coder 30B A3B Instruct is a code-specialized model from Alibaba's Qwen3 series. It features a 30B total parameter size with a 3B active parameter architecture (likely a Mixture-of-Experts design), optimized for code generation, understanding, and instruction following.

阿里巴巴

Qwen3 Coder 480B A35B Instruct is a large-scale, code-specialized language model from Alibaba's Qwen series. It features a Mixture-of-Experts (MoE) architecture with 480 billion total parameters and 35 billion active parameters, designed for high-performance code generation, understanding, and instruction following.

阿里巴巴

Alibaba Group, through its Alibaba Cloud division, develops and offers the Qwen series of large language models for AI applications. The Qwen3-Coder-Next model is a coding-focused AI model with strong agentic capabilities, trained on executable task synthesis and reinforcement learning. Alibaba provides AI platform services via Alibaba Cloud Model Studio.

阿里巴巴

Qwen3 Max is Alibaba Cloud's flagship large language model, designed for high-performance general tasks. It features strong multimodal understanding, a 128K long context window, and excels in complex reasoning and code generation.

阿里巴巴

Qwen3 Max (Preview) is the latest flagship model from Alibaba's Qwen series, designed for high-performance enterprise applications. It features enhanced reasoning and coding capabilities, supports an ultra-long context window, and is optimized for complex analytical tasks.

阿里巴巴

Qwen3 Max Thinking is a high-end model from Alibaba's Qwen3 series, optimized for complex reasoning tasks. It features an enhanced thinking mode for deeper analysis and supports long-context processing.

阿里巴巴

Qwen3 Max Thinking (Preview) is an advanced AI model from Alibaba, designed for enhanced reasoning and chain-of-thought capabilities. It excels in complex problem-solving and logical tasks, with a focus on deep thinking modes.

阿里巴巴

Qwen3 Next 80B A3B (Reasoning) is a large language model from Alibaba's Qwen series, optimized for complex reasoning tasks. It utilizes a Mixture-of-Experts (MoE) architecture with 80 billion total parameters but only 3 billion active parameters per inference, offering a strong balance between high performance and computational efficiency.

阿里巴巴

Qwen3 Next 80B A3B Instruct is a large language model from Alibaba's Qwen series, featuring a sparse activation architecture (likely 80B total parameters with ~3B active parameters per token). This design aims to deliver strong reasoning and coding capabilities while significantly improving inference speed and cost-efficiency compared to dense models of similar total size.

阿里巴巴

Qwen3 Omni 30B A3B (Reasoning) is a multimodal model from Alibaba's Qwen3 series, optimized for complex reasoning tasks. It processes both text and images, leveraging a 30-billion parameter architecture with 3 billion active parameters for efficient inference.

阿里巴巴

Qwen3 Omni 30B A3B Instruct is a multimodal model from Alibaba's Qwen3 series, designed for instruction-following tasks. It features a 30-billion parameter architecture with 3 billion active parameters, balancing performance and efficiency. The model supports both text and image inputs, making it suitable for diverse multimodal applications.

阿里巴巴

A large vision-language model from Alibaba with 235B total parameters and 22B activated parameters, focused on enhanced reasoning capabilities. It combines visual understanding with language generation, suitable for complex multimodal tasks requiring reasoning.

阿里巴巴

Qwen3 VL 235B A22B Instruct is a large-scale multimodal model from Alibaba's Qwen series, featuring a 235B total parameter Mixture-of-Experts (MoE) architecture with 22B active parameters. It is designed for advanced visual and language understanding tasks, offering strong reasoning capabilities while maintaining efficiency.

阿里巴巴

A multimodal vision-language model from Alibaba's Qwen3 series, optimized for reasoning tasks. It features a 30B total parameter architecture with 3B activated parameters, suggesting a Mixture-of-Experts design for efficient inference.

阿里巴巴

Qwen3 VL 30B A3B Instruct is a multimodal vision-language model from Alibaba's Qwen3 series. It is designed to process both image and text inputs, likely leveraging a Mixture-of-Experts architecture (30B total parameters, 3B active) for efficient inference. The model is instruction-tuned for following user prompts in visual and language tasks.

阿里巴巴

Qwen3 VL 32B (Reasoning) is a multimodal vision-language model from Alibaba's Qwen series, optimized for complex reasoning tasks. It integrates visual understanding with strong logical and analytical capabilities, suitable for tasks requiring visual input and step-by-step reasoning.

阿里巴巴

A 32-billion parameter vision-language model from Alibaba's Qwen3 series, excelling at image understanding, visual question answering, and multimodal reasoning while maintaining good response speed and cost efficiency.

阿里巴巴

A compact 4-billion parameter multimodal model from the Qwen3 VL series, optimized for visual reasoning tasks. It processes both images and text to perform complex reasoning, making it suitable for applications requiring visual understanding and logical inference.

阿里巴巴

A lightweight vision-language model from Alibaba's Qwen3 series with 4 billion parameters. It is designed for efficient image understanding and multimodal tasks, offering fast inference and low deployment costs, suitable for edge or resource-constrained scenarios.

阿里巴巴

Qwen3 VL 8B (Reasoning) is a lightweight, multimodal vision-language model from Alibaba's Qwen series, optimized for enhanced reasoning capabilities. It efficiently processes both text and images, making it suitable for tasks requiring visual understanding and logical inference.

阿里巴巴

Qwen3 VL 8B Instruct is a lightweight, 8-billion parameter vision-language model from Alibaba's Qwen series. It is designed for efficient multimodal understanding and instruction following, excelling at tasks that require processing both text and images.