Qwen3.5
Qwen3.5 is a next-generation open-weight multimodal large language model designed to power native vision-language agents. The flagship release, Qwen3.5-397B-A17B, combines a hybrid linear attention architecture with sparse mixture-of-experts, activating only 17 billion parameters per forward pass out of 397 billion total to maximize efficiency. It delivers strong benchmark performance across reasoning, coding, multilingual understanding, visual reasoning, and agent-based tasks. The model expands language support from 119 to 201 languages and dialects while introducing a 1M-token context window in its hosted version, Qwen3.5-Plus. Built for multimodal tasks, it processes text, images, and video with advanced spatial reasoning and tool integration. Qwen3.5 also incorporates scalable reinforcement learning environments to improve general agent capabilities. Designed for developers and enterprises, it enables efficient, tool-augmented, multimodal AI workflows.
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Qwen3.5-Plus
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.
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Aya Vision
Aya Vision is a research model advancing in multilingual multimodal AI through innovative synthetic data generation, cross-modal model merging, and a comprehensive benchmark suite. It achieves state-of-the-art performance across 23 languages, surpassing larger models while efficiently addressing data scarcity and catastrophic forgetting by reducing computational overhead up to 40% via optimized training techniques.
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Qwen3.7-Plus
Qwen3.7-Plus is a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7’s agentic intelligence, it extends Qwen’s capabilities into visual understanding, visual reasoning, grounded interaction, and multimodal tool use, enabling agents to perceive, analyze, and act across text, images, documents, screens, and complex real-world contexts. It is designed for tasks that require more than static question answering, including visual search, document comprehension, chart and table analysis, screen understanding, GUI interaction, image-grounded reasoning, and agent workflows that combine perception with planning and execution. Qwen3.7-Plus strengthens the connection between language reasoning and visual evidence, allowing users to ask questions about images, interpret dense multimodal inputs, extract structured information, and generate responses that reflect both context and visual details.
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