There Is No Single Strongest Model: ChatGPT Has to Be Chosen Again for Every Task

The “There Is No Single Strongest Model: ChatGPT Has to Be Chosen Again for Every Task” scene leads to a working conclusion: the main problem is no longer a shortage of powerful models but the fact that the user has to work out for themselves which of GPT-4o, o3 Pro, Deep Research, Gemini, and Claude fits a given task and exactly where it will start to fail.

For the “Different ChatGPT models under different objectives” scene, the decisive point is this: the argument about the “best” model falls apart the moment a real task replaces a ranking — one model is handier for a long text, another for deep research, a third for connecting Google Drive.

The decision in “Why is O3 better than GPT-4O?” depends on one criterion: even inside ChatGPT itself the user must manually pick between GPT-4o, o3, and Deep Research, though a mature product should decide on its own which mode a given request needs.

The working conclusion from “Anthropic and the "think" tool: how does it work?” is that the central complaint against OpenAI is this: it shipped many strong models but left the user the dispatcher's job — remembering names, limits, context length, price, and reasoning style; and when the result is poor, it is unclear whether the model, the wrong mode, or an interface that dropped part of the request is to blame.

In the context of “ChatGPT memory in the free version,” this criterion applies: expanded memory in the free tier is a real step — the chat starts drawing on earlier conversations — but its value runs into the fact that OpenAI keeps data only thirty days and does not retrieve the older chats a user wants to return to.

The “DeepResearch and Google Drive - First Test” issue should be assessed with one constraint in mind: once research can run across Google Drive and a person’s own materials, the model stops being merely a conversational partner and starts working with the real archive of an individual or a company. That immediately raises questions about data retention, thirty-day limits, file access, and why an ordinary use case requires connecting third-party services.

The discussion of “Adding third-party services to AI” yields a practical test: connecting outside services turns Deep Research into a build-it-yourself rig — through MCP you can add, say, Whisper-based transcription and feed the model the audio it cannot read on its own; powerful, but it is the “iPhone versus Android” trade-off, where everything has to be wired up by hand.

In the context of “AI hallucinations: do people err more often?,” this criterion applies: the garden-advice story captures the real point about hallucinations — the model gave a detailed diagnosis and was right where a person was wrong, yet an answer still cannot be trusted without checking: like human advice, it should be verified, especially where the cost of error is high.

For the decision discussed in “Apple opens up its AI models,” one criterion matters: Apple is opening its AI models to outside developers, but the question is which models — the company is far behind, insiders say it is not making acquisitions yet, and Google is already putting local Gemma models on devices; another promise for next year instead of a product would only widen the gap.

In the context of “Alibaba: impact on the future of AI,” this criterion applies: the paradox is that Alibaba's open models push American generative-AI startups further than the closed models of OpenAI, Google, and Anthropic — closed models are convenient for starting on investor money, but optimizing for mass use cases needs open source, and DeepSeek lacks the “cache-printing machine” Alibaba has.