42 models
Qwen3.8 Max Prime
qwen/qwen3.8-max-primeQwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. It accepts text, image, and video...
qwenQwen3.8 Max (0902)
qwen/qwen3.8-max-0902Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input and returns text,...
qwenQwen3.8 Flash
qwen/qwen3.8-flashQwen3.8 Flash is a multimodal reasoning model from Alibaba. It is suited for coding assistance, agentic workflows, visual understanding, document and codebase analysis, desktop interaction, chart analysis, and long-video analysis.
qwenQwen3.8 27B
qwen3.8-27bQwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be...
qwenQwen3.8 27B (free)
qwen/qwen3.8-27b:freeQwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be...
qwenQwen3.8 2.4T A95B
qwen3.8-2.4t-a95bQwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total. It is...
qwenQwen3.8 Max
qwen3.8-maxAlibaba's flagship Qwen3.8 model: a 2.4T-parameter MoE (95B active) multimodal reasoning model with a 1M-token context window.
qwenQwen3.7 Plus
qwen/qwen3.7-plusQwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...
qwenQwen3.7 Max
qwen3.7-maxQwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...
qwenQwen3.6 Max Preview
qwen/qwen3.6-max-previewQwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...
qwenQwen3.6 Plus
qwen/qwen3.6-plusQwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...
qwenQwen3.5-122B-A10B
qwen/qwen3.5-122b-a10bThe Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.
qwenQwen3.5-27B
qwen/qwen3.5-27bThe Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance.
qwenQwen3.5-35B-A3B
qwen/qwen3.5-35b-a3bThe Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency.
qwenQwen3.5-Flash
qwen/qwen3.5-flash-02-23The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.
qwenQwen3.5 397B A17B
qwen/qwen3.5-397b-a17bThe Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.
qwenQwen3 Max
qwen/qwen3-maxQwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version.
qwenQwen3 VL 235B A22B Instruct
qwen3-vl-235b-a22b-instructQwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video.
qwenQwen3 VL 235B A22B Thinking
qwen/qwen3-vl-235b-a22b-thinkingQwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math.
qwenQwen3 Next 80B A3B Instruct
qwen3-next-80b-a3b-instructQwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces.
qwenQwen3 Next 80B A3B Instruct (free)
qwen/qwen3-next-80b-a3b-instruct:freeQwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces.
qwenQwen3 Next 80B A3B Thinking
qwen/qwen3-next-80b-a3b-thinkingQwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default.
qwenQwen3 30B A3B Thinking 2507
qwen/qwen3-30b-a3b-thinking-2507Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking.
qwenQwen3 Coder 30B A3B Instruct
Qwen3-Coder-30B-A3B-InstructQwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use.
qwenQwen3 30B A3B Instruct 2507
qwen/qwen3-30b-a3b-instruct-2507Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference.
qwenQwen3 235B A22B Thinking 2507
qwen/qwen3-235b-a22b-thinking-2507Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks.
qwenQwen3 Coder 480B A35B
qwen/qwen3-coderQwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories.
qwenQwen3 235B A22B Instruct 2507
qwen/qwen3-235b-a22b-2507Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass.
qwenQwen3 235B A22B
qwen/qwen3-235b-a22bQwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass.
qwenQwen3 30B A3B
qwen/qwen3-30b-a3bQwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks.
qwenQwen3 32B
qwen/qwen3-32bQwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue.
qwenQwen2.5 VL 32B Instruct
qwen/qwen2.5-vl-32b-instructQwen2.5-VL-32B is a multimodal vision-language model fine-tuned through reinforcement learning for enhanced mathematical reasoning, structured outputs, and visual problem-solving capabilities.
qwenQwQ 32B
qwen/qwq-32bQwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problem…
qwenQwen2.5 VL 72B Instruct
qwen/qwen2.5-vl-72b-instructQwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
qwenQwen-Max
qwen/qwen-maxQwen-Max, based on Qwen2.5, provides the best inference performance among [Qwen models](/qwen), especially for complex multi-step tasks.
qwenQwen-Plus
qwen/qwen-plusQwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.
qwenQwen VL Max
qwen/qwen-vl-maxQwen VL Max is a visual understanding model with 7500 tokens context length. It excels in delivering optimal performance for a broader spectrum of complex tasks.
qwenQwen2.5 Coder 32B Instruct
qwen/qwen-2.5-coder-32b-instructQwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reaso…
qwenQwen2.5 72B Instruct
qwen/qwen-2.5-72b-instructQwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized…
qwenqwen3.6-27b
qwen3.6-27bGlobal: Qwen 3.6 27B
qwenQwen3.6 35B-A3B
japan-qwen3.6-35bJapan: Qwen 3.6 35B-A3B - hosted and run in Japan
qwenQwen3 Coder 480B A35B
Qwen3-Coder-480B-A35B-Instruct-FP8Alibaba's most capable open-source agentic coding model. A Mixture-of-Experts architecture with 480B total parameters (35B active), trained on 7.5 trillion tokens.
qwen