ChatBetter

ChatBetter's headline promise was that you didn't need a separate tab or subscription for each model. A single prompt could reach OpenAI, Anthropic, Google, xAI, DeepSeek, Meta, Perplexity, Mistral, and Cohere through the same chat box. For someone paying for three or four subscriptions, that consolidation was the main draw.

Rather than making users guess which model to pick, ChatBetter routed each request on its own. It judged the task and sent the prompt to the model, or mix of models, most likely to answer well. The team said this routing kept answers accurate while also trimming cost, since simpler questions didn't need the most expensive model.

When a question mattered, users could send it to several models at once and read the answers in columns. From there, ChatBetter could merge the strongest parts into a single reply. The team described this as getting a second opinion without leaving the page, and it was the feature users compared most often to using several apps at once.

ChatBetter rewrote or sharpened prompts before sending them. A user typing a rough question would get a cleaned-up version that the models could act on more precisely. Over time the platform tracked which prompts worked, so a team could reuse a proven phrasing instead of rediscovering it.

Chats weren't just a flat list. Users could group conversations by project or client, search across history, and adjust the context a model could see. On team plans, a shared library let colleagues borrow prompts that had already worked, which cut the time spent reinventing instructions.

Beyond the free tier sat a set of features aimed at companies. Those included single sign-on, role-based access, audit logs, tenant isolation, and connections to tools like Slack, Microsoft Teams, and Databricks. The pitch was that a company could adopt several models without letting every employee roam unchecked.