32K
0.26
0.78
235B
Active
Qwen 3 235B A22B
Alibaba Cloud’s Qwen 3 blends dense and MoE architectures for superior coding, multilingual support, and long-context processing across 119 languages, powering efficient enterprise solutions.
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Advanced multilingual AI with 128K-token context, excelling in coding, reasoning, and enterprise applications.
Qwen3-235B-A22B, developed by Alibaba Cloud, is a flagship large language model leveraging a Mixture-of-Experts (MoE) architecture. With 235 billion total parameters and 22 billion active per inference, it delivers top-tier performance in coding, math, and reasoning across 119 languages. Optimized for enterprise tasks like software development and research, it’s accessible via AI/ML API.
Qwen3-235B-A22B uses a Transformer-based MoE architecture, activating 22 billion of its 235 billion parameters per token via top-8 expert selection, reducing compute costs. It features Rotary Positional Embeddings and Group-Query Attention for efficiency. Pre-trained on 36 trillion tokens across 119 languages, it uses RLHF and a four-stage post-training process for hybrid reasoning.
Qwen3-235B-A22B excels in hybrid reasoning, toggling between thinking mode (/think) for step-by-step problem-solving and non-thinking mode (/no_think) for rapid responses. It supports 119 languages, enabling seamless global applications like multilingual chatbots and translation. With a 128K-token context, it processes large datasets, codebases, and documents with high coherence, using XML delimiters for structure retention.
Qwen3-235B-A22B is tailored for high-complexity enterprise scenarios requiring deep reasoning and scalability:
Qwen3-235B-A22B stands out among leading models due to its MoE efficiency and multilingual capabilities:
Qwen3-235B-A22B integrates via AI/ML API. Documentation available here.
Qwen3-235B-A22B, developed by Alibaba Cloud, is a flagship large language model leveraging a Mixture-of-Experts (MoE) architecture. With 235 billion total parameters and 22 billion active per inference, it delivers top-tier performance in coding, math, and reasoning across 119 languages. Optimized for enterprise tasks like software development and research, it’s accessible via AI/ML API.
Qwen3-235B-A22B uses a Transformer-based MoE architecture, activating 22 billion of its 235 billion parameters per token via top-8 expert selection, reducing compute costs. It features Rotary Positional Embeddings and Group-Query Attention for efficiency. Pre-trained on 36 trillion tokens across 119 languages, it uses RLHF and a four-stage post-training process for hybrid reasoning.
Qwen3-235B-A22B excels in hybrid reasoning, toggling between thinking mode (/think) for step-by-step problem-solving and non-thinking mode (/no_think) for rapid responses. It supports 119 languages, enabling seamless global applications like multilingual chatbots and translation. With a 128K-token context, it processes large datasets, codebases, and documents with high coherence, using XML delimiters for structure retention.
Qwen3-235B-A22B is tailored for high-complexity enterprise scenarios requiring deep reasoning and scalability:
Qwen3-235B-A22B stands out among leading models due to its MoE efficiency and multilingual capabilities:
Qwen3-235B-A22B integrates via AI/ML API. Documentation available here.
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