Abandon ChatGPT for Every Task: Which AI Models I Prefer for Research, Coding, Etc., and Which I Steer Clear Of

The world of generative AI is advancing so quickly, it feels impossible to keep up. Naming inconsistencies among AI models add to the confusion. Models like GPT-5.1, Opus 4.5, and Gemini 3 sound mysterious, don't they? Deciphering their differences is not for the faint-hearted.

Trying to unravel each model's intricacies might drive you insane. Instead, focus on the specific tasks each excels at. That's the goal here: guiding you to pick the best AI for your needs.

Cost vs. Return on Investment

AI companies typically offer their models as APIs and as chatbot tools. Keep in mind, though, a chatbot is essentially a chat interface harnessing an AI model's intelligence.

Initially, free versions are available, but frequent use often leads to about $20/month subscriptions for full features.

Many software providers have jumped on the AI bandwagon. As a long-time QuickBooks user, I've seen how integration might also mean extra charges for AI capabilities, even if you already subscribe to AI plans elsewhere.

Your decision on which AI to use should be application-focused, not model-focused. Here's how I typically integrate these models into my workflow, prioritizing the app and letting the model naturally follow.

Crafting Educational Summaries

An exciting integration I've found invaluable is NotebookLM, which I started using routinely about six months ago. It's primarily a note-taking tool equipped with a language model, but its standout feature is creating audio summaries of the given material.

You have the option of plain audio or audio with visuals. I usually opt for just the audio. Whenever I'm faced with a complex paper or report, I input it into NotebookLM. A short while later, it delivers a concise discussion on the main topics covered.

Keep this in mind: the output is never used verbatim for my work. It's simply a way to grasp the document's overarching themes quickly.

While Google doesn’t specify, the underlying model of NotebookLM appears to be a version of Gemini, possibly the recent Gemini 2.5.

Finding Crucial Keywords

Updating AI models is inevitable, hence I don't focus heavily on specific versions. After a few months, models evolve.

For keyword generation, I've turned to Karakeep, a self-hosted web archiving tool, to catalog articles for future reference. Using OpenAI's API, Karakeep automatically generates keywords efficiently, a feature lacking in Pocket, my previous choice.

Transferring my vast collection of over 24,000 articles from Pocket to Karakeep took time and about $40. Ongoing costs are minimal, roughly $5 every couple of months.

Programming

For writing and troubleshooting code, nothing surpasses ChatGPT Plus with GPT-5.1. However, for more interactive coding where AI assists with substantial project insights, OpenAI’s Codex coupled with GPT-5.1-Max and Claude Code using Opus 4.5 are exceptional.

I utilized Codex to develop a comprehensive project, and with its help, completed it in short sessions over a couple of weeks. This endeavor cost me $300 across both tools, but the efficiency was worth the investment.

Organizing Notion Databases

Despite my reservations regarding Notion's AI upsell tactics, I eventually joined their yearly plan. This came out slightly cheaper at $20 monthly when paid annually.

I mainly use Notion AI to search and summarize draft articles. This becomes essential as I review past work without external AI access.

It also organizes extensive lists into databases, which I found useful for managing AI tool directories. It categorizes these efficiently, offering a practical, although imperfect, solution.

Transcribing Speech

Given my loveable pup's preference for resting on my shoulder, typing can be laborious. Instead, I frequently rely on my Mac's native dictation to transcribe thoughts.

Recently, I've explored more enhanced dictation software like Paraspeech, which operates local AI models effectively. It’s a one-time purchase, avoiding recurring subscription fees.

Intensive Research

AI deep research consumes considerable resources, often reflecting in the price tag. Earlier, when subscribed to an expansive Pro programming tier, I accomplished remarkable feats.

In a particularly memorable project, AI analyzed source code to draft marketing briefs. It deciphered the code intricacies and formulated comprehensive documentation, sparing me numerous hours of work.

Everyday Analysis and SEO

For everyday business analytics, I lean heavily on ChatGPT's Plus version. Its adaptability makes it indispensable for data scrutiny and SEO optimization.

While its default is helpful, I’m eyeing Gemini 3, recently launched with promise. Future updates might see it challenging ChatGPT’s current predominance.

Avoided Tools and Models

Models like Perplexity, Microsoft's Copilot, and Grok haven't impressed me. Perplexity's search functions underwhelmed, and Copilot's Microsoft-centric utility isn't a fit for my needs.

Grok had success in testing, but alternatives like Codex overshadowed its performance in practical scenarios.

Your Turn to Share

What’s your experience? Do you vary your AI tools based on tasks, or stick to a preferred one? Have any models particularly impressed or disappointed you? Share your thoughts with us.