qwen3.5-27bQwen3.5-27B is a native vision-language dense model that incorporates a linear attention mechanism to deliver fast response times while maintaining a strong balance between inference speed and overall performance. Despite its smaller scale, its overall capabilities are comparable to Qwen3.5-122B-A10B, making it an efficient and practical choice for multimodal applications that require both responsiveness and high-quality reasoning.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="qwen3.5-27b", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="qwen3.5-27b",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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Qwen3.7-Plus is a cost-effective multimodal model in Alibaba's Qwen3.7 series, supporting text and image inputs with text output. It combines the series' strong language capabilities with significantly enhanced vision-language understanding, while retaining full-stack agent-level intelligence for coding, tool use, and productivity workflows. Its standout capability is multimodal interactive agency—the ability to perceive real-world scenes, understand screens and graphical interfaces, generate code from visual references, and perform end-to-end navigation within applications. This makes Qwen3.7-Plus well suited for GUI automation, visual coding, productivity agents, and multimodal task execution.
Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series, designed for agent-centric workloads with strong performance in coding, productivity, and long-horizon autonomous execution. It supports text input and output and delivers notable improvements in coding and agentic capabilities over previous Qwen generations. Optimized for real-world workflows, the model also supports explicit prompt caching for efficient reuse of repeated context, making it well suited for scalable development, office automation, and advanced agent systems.
Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse Mixture-of-Experts (MoE) architecture with approximately 1 trillion parameters. It is optimized for agentic coding, tool use, and long-context reasoning, supporting a 262K token context window. The model includes an integrated thinking mode that preserves reasoning across multi-turn interactions, along with support for structured outputs and function calling. Available exclusively via Alibaba Cloud Model Studio and Qwen Studio APIs, it is designed for high-performance, production-grade agent workflows.
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Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse Mixture-of-Experts (MoE) architecture with approximately 1 trillion parameters. It is optimized for agentic coding, tool use, and long-context reasoning, supporting a 262K token context window. The model includes an integrated thinking mode that preserves reasoning across multi-turn interactions, along with support for structured outputs and function calling. Available exclusively via Alibaba Cloud Model Studio and Qwen Studio APIs, it is designed for high-performance, production-grade agent workflows.
Tongyi DeepResearch is a 30B-parameter agentic model (3B active per token) built for long-horizon, deep research and information-seeking tasks. It achieves state-of-the-art results on major agentic search and reasoning benchmarks, outperforming prior models in complex multi-step problem solving. Trained with a fully automated synthetic data pipeline and advanced on-policy RL, it supports ReAct workflows and a high-performance “Heavy” mode for test-time scaling, making it well suited for advanced research agents, tool use, and intensive inference workloads.
Qwen3-Max-Thinking is Alibaba's latest flagship reasoning-enhanced large language model, evolving the Qwen3-Max architecture to emphasize deep, multi-step analytical reasoning and tool collaboration. It scales the model's capacity significantly—reportedly to over 1 trillion parameters—and integrates a “Thinking Mode” where the model can expose and leverage step-by-step reasoning traces before producing final answers, enabling more reliable solutions to complex problems such as advanced mathematics, logic, and multi-stage tasks.
Qwen3-VL-8B-Thinking is the reasoning-focused version of the Qwen3-VL-8B multimodal model, built for advanced visual and textual reasoning across images, documents, and video. It adds deeper vision-language fusion and deliberate reasoning paths, supports very long context (256K–1M tokens), and excels at STEM problem solving, causal analysis, and multi-step video understanding — while retaining strong OCR, multilingual capability, and high-quality text generation.
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