qwen3-vl-235b-a22b-thinkingQwen3-VL-235B-A22B Thinking is a powerful multimodal model that combines advanced text generation with strong image and video understanding, optimized for STEM and math reasoning. It offers robust perception, spatial grounding, and long-form visual comprehension, and supports agent-style interactions such as multi-image dialogue, video timeline alignment, GUI control, and visual-to-code workflows. With competitive benchmark results and strong text-only ability, it’s suited for production uses like document AI, OCR, UI assistance, spatial tasks, and vision-language research.
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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-235b-a22b-thinking", 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-235b-a22b-thinking",# 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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Qwen3.6-35B-A3B is an open-weight Mixture-of-Experts (MoE) multimodal model designed for agentic coding and long-horizon workflows. It features ~35–36B total parameters with ~3B activated per token, enabling strong performance with high inference efficiency. The model supports text and image inputs with a ~260K token context window, and is optimized for repository-level reasoning, multi-step development, and tool-driven workflows. With strong benchmark performance and improved coherence across extended tasks, Qwen3.6-35B-A3B is well suited for developer tools, coding agents, and real-world engineering applications that require both reasoning depth and efficiency.
Qwen3.6 Flash is a fast and efficient model from Alibaba's Qwen 3.6 series, supporting text, image, and video inputs with a 1M-token context window for high-context multimodal workflows. Optimized for performance and cost efficiency, it features tiered pricing beyond 256K tokens and supports prompt caching with both cache creation and read pricing, making it well suited for large-scale, high-throughput applications.
Qwen3-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.
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.
No observed failures in the current observation window