Every frontier model. One conversation.

Paying for ChatGPT, Claude, Gemini and Perplexity separately means four subscriptions, four histories, and four places to check. Unify puts them behind one login and one credit balance. GPT is the strongest generalist for reasoning and code. Claude handles long documents and careful writing with the most consistent tone. Gemini is fast and strong on multimodal input. Grok is direct and current. Perplexity Sonar searches the live web and cites what it used. DeepSeek is a capable low-cost option for bulk work. You are not locked into whichever one you happened to subscribe to first.

The most useful thing about having every model in one place is that you do not have to choose upfront. Start a thread with a fast model while you are still shaping the question, then switch to a stronger one when the problem gets hard, without re-pasting context. When an answer looks confident but you are unsure, put the identical question to a second model and compare. Disagreement between two models is a reliable signal that the question is harder than it looks, or that the answer depends on something you have not specified.

Use a search-grounded model — Perplexity Sonar — whenever the answer depends on something that changed recently, because a model answering from training data will sound equally confident whether or not it knows. For code, GPT and Claude are both strong, and Grok Code Fast is worth trying when you want speed over depth. For long documents, summarisation, and anything where tone matters, Claude tends to hold structure best across thousands of words. For quick factual lookups and multimodal input, Gemini is fast and inexpensive. When cost is the constraint and the task is routine, DeepSeek does most of it for a fraction of the credits.

The quality of an answer is mostly set by the question. State the decision you are trying to make, not just the topic. Supply the constraints — audience, budget, timeline, what you have already ruled out — because a model cannot infer them and will otherwise give you the generic answer. Specify the output format you want. Then ask the model to state its assumptions and flag anything it is unsure about, which turns a confident-sounding paragraph into something you can actually check. For consequential questions, ask a second model the same thing before you act.