qwen3-vl-32b-instructQwen3-VL-32B-Instruct is a 32B-parameter multimodal model built for precise reasoning across text, images, and video. It combines strong perception with advanced language understanding for tasks like spatial reasoning, document and scene analysis, and long video comprehension. With robust OCR in 32 languages and enhanced fusion architectures, it’s optimized for agent-style interaction and visual tool use, delivering state-of-the-art results on complex real-world multimodal tasks.
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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-vl-32b-instruct", 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-vl-32b-instruct",# 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 is the latest generation in the Qwen large language model series, featuring both dense and Mixture-of-Experts (MoE) architectures designed to excel in reasoning, multilingual understanding, and advanced agentic tasks. A defining capability of Qwen3 is its ability to seamlessly switch between a thinking mode for complex, multi-step reasoning and a non-thinking mode for efficient, high-quality dialogue—delivering strong versatility across use cases. Compared with earlier models such as QwQ and Qwen2.5, Qwen3 demonstrates substantial performance gains in mathematics, coding, commonsense reasoning, creative writing, and interactive conversation. The Qwen3-30B-A3B variant comprises 30.5B total parameters with 3.3B activated, 48 layers, and 128 experts (with 8 activated per task). With support for up to 131K token context lengths via YaRN, it sets a new benchmark for open-source MoE models in both capability and efficiency.
Qwen3.5-122B-A10B is a native vision-language model built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design for improved inference efficiency. In overall performance, it ranks just below Qwen3.5-397B-A17B, delivering substantial gains over previous generations. Its text capabilities significantly exceed Qwen3-235B-2507, while its visual performance surpasses Qwen3-VL-235B, making it a strong high-end option for advanced multimodal and agent-driven applications.
Qwen3.6-27B is an open-weight 27B-parameter dense multimodal model from the Qwen3.6 series, designed to deliver flagship-level coding and agentic performance at a practical deployment scale. It supports both text and image inputs and introduces improvements in agentic coding, repository-level reasoning, and iterative development workflows. Despite its relatively compact size, it achieves state-of-the-art results on coding benchmarks, outperforming much larger models in tasks such as SWE-bench and terminal-based workflows. It also provides strong reasoning and multimodal capabilities, along with features like thinking preservation to maintain context across interactions, making it well suited for developer tools, coding agents, and real-world engineering tasks.
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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