About Q-Chat

What Q-Chat Is

Q-Chat is an independent AI chat product for trying models in the Qwen family from a browser. The site focuses on practical model selection: which Qwen model is faster, which one is better for reasoning, which route is hosted, and when a local or open-weight path makes more sense.

The product is operated at qwen35.com and is not affiliated with, endorsed by, or sponsored by Alibaba Cloud or the Qwen team. Qwen names, model names, and related marks belong to their respective owners.

Who Q-Chat Is For

Q-Chat is built for developers, researchers, and teams evaluating Qwen models before integrating them into production workflows. Whether you are building a customer-facing chatbot, an internal coding assistant, a document summarization pipeline, or a multimodal application, the site helps you narrow down which Qwen model fits your speed, accuracy, and cost requirements.

Individual users exploring AI assistants for writing, learning, or creative work will also find the browser-based chat useful for comparing model responses side by side without installing anything locally.

What This Site Publishes

The public pages on qwen35.com are written for people comparing Qwen models before using them in real work. We publish:

• model pages for Qwen 3.5, Qwen 3.6, and Qwen 3.7 routes available in the product, each covering context window, access method, recommended use cases, and known limitations;
• practical blog posts about API usage, benchmarks, local deployment with Ollama, vLLM, GGUF, and Hugging Face, hosted chat experiences, and model tradeoffs across different task types;
• policy pages that explain privacy, terms, acceptable use, refunds, and support contact paths.

When a model capability is based on official documentation or a public release post, we try to describe it as a sourced claim. When something depends on a user's workload, we frame it as a testing recommendation rather than a guaranteed result.

Editorial Approach

Q-Chat content is intentionally practical. Instead of repeating only launch language, pages compare nearby model choices and explain where a model is likely to fit:

• speed-focused chat and prompt iteration, where latency matters more than peak accuracy;
• coding, debugging, and agent workflows that require multi-step reasoning and tool use;
• long-context reading and document synthesis for reports, contracts, and research papers;
• image or multimodal understanding for visual question answering and diagram interpretation;
• local deployment routes such as Ollama, vLLM, GGUF, and Hugging Face for users who need offline access or data privacy.

Every model page includes a source notes section that links to the official Qwen release materials. We encourage readers to verify claims against those primary sources, especially for technical specifications like context length, parameter counts, and supported modalities.

The goal is to help visitors make a model choice, test that model on their own prompts, and understand limitations before they rely on it in production.

How We Handle Model Information

The Qwen model family evolves quickly. New models, updated benchmarks, and revised API endpoints appear regularly. Q-Chat tracks these changes and updates model pages when official sources confirm new information.

We do not publish unverified leaks or speculative benchmark results. If a model capability has not been confirmed by the Qwen team or Alibaba Cloud, we either omit it or clearly label it as unconfirmed.

Product and Support

Q-Chat provides browser-based access to selected Qwen models and related account features. Some features may require login, credits, or a paid plan. Billing, refund, acceptable-use, and privacy details are described in the policy pages linked from the footer.

For support questions, contact support@qwen35.com. For legal and data questions, start with the Privacy Policy, Terms of Service, Acceptable Use Policy, and Refund Policy.