Choosing the Best AI Models for Research and Coding: A Guide Beyond ChatGPT

In the rapidly evolving world of generative AI, it can be daunting to keep up with the myriad of AI models like GPT, Claude, and Gemini. Understanding which models suit different tasks is crucial rather than being bewildered by their updates and versions.

Understanding AI Models and Applications

AI models are essentially the engines powering various intelligent tasks, while applications are the tools used to interact with these models. For instance, a diagram created via Google's software gives a decent illustration of this concept.

Different applications rely on diverse AI models, much like how distinct vehicles operate with varied engines. An example: using the same prompt, ChatGPT created a straightforward image, while Midjourney produced a more elaborate one but missed the prompt's intent.

Economic Aspects of AI

AI models are often sold both as integrated packages in chatbot software and standalone APIs for developers to embed in their apps. It's important to recognize that a chatbot like ChatGPT is essentially a messaging application tapping into an AI model for intelligence.

With most software vendors embedding AI features, additional fees are usually involved, even if you're already subscribed to an AI service.

Choose models based on your needs rather than the version or name. I typically focus on the task and app first, letting the model be a byproduct of that choice.

Tools for Crafting Explanations

A prime tool in my arsenal is NotebookLM. Though it functions as a notebook, its standout feature is creating audio-based explanations from source content, proving invaluable when dissecting dense documents.

NotebookLM, likely powered by a Gemini variant, excels because of its audio explainer function, which I use to unpack complex material swiftly.

Keyword Identification

For generating keywords, I utilize Karakeep, a self-hosted solution that automates keyword creation via the OpenAI API. This tool has effectively replaced Pocket for my research article storage needs.

Transitioning to Karakeep from Pocket was time-consuming but ultimately cost-effective, providing comprehensive automated keyword tagging via OpenAI's technology.

AI in Coding

For coding, GPT-5.1 in ChatGPT Plus is unmatched for troubleshooting code snippets. However, for more complex, agentic tasks, Codex and Claude Code provide remarkable results, facilitating extensive operations within my development environment.

These tools are unparalleled for larger coding operations and enhanced my ability to perform comprehensive programming tasks significantly.

Utilizing Notion for Database Management

I use Notion AI to search and summarize my article drafts despite its AI upsell focus. It's instrumental in organizing large datasets into manageable formats, even if it requires higher-tier subscriptions.

Utilizing Notion's varying models, from Claude to ChatGPT, allows dynamic responses to tasks, optimizing cost and performance.

Advanced Speech Recognition

For dictation, I’ve started using Paraspeech. This application runs AI models locally, providing robust speech recognition technologies without recurring fees—a beneficial economic model.

In-Depth Research

My use of Deep Research exemplifies AI’s power in synthesizing extensive software codebases into cohesive briefing documents. This capability dramatically cuts down on manual labor and fosters efficiency.

Merging these insights with NotebookLM’s functionality further streamlines complex project workflows and documentation creation processes.

Evaluating General AI Tool Use

For broad support tasks, ChatGPT Plus excels with its versatility. Whether engaging in data analysis or selecting SEO keywords, it serves as a reliable and consistent tool.

While experimenting with new models like Gemini 3, the landscape of general-purpose AI tools continues to evolve.

Preferred and Avoided Tools

Though I don't favor models like Perplexity or Copilot due to personal preferences and use cases, they may serve others’ needs well.

Apple’s current offerings lack robust AI integrations, an area they need to develop significantly to stay competitive in this space.

Reflecting on Model Selection

How do your choices in AI tools vary based on specific tasks? Are there models or tools you swear by for certain functions like images, coding, or research? Share your experiences and perspectives to broaden our collective understanding.