Best OpenAI docs to bookmark: OpenAI model guide. Official OpenAI guide to current GPT, reasoning, coding, and tool-capable models. Use it when model names, reasoning levels, and API behavior matter.
Best Claude model reference: Claude models overview. Official Anthropic overview of Claude models and release families. Use it before relying on secondary summaries of Claude capabilities.
Best Gemini model reference: Gemini API models. Official Google AI for Developers model documentation. Use it to compare Gemini context, modality, and release channels.
Primary sources matter most when models move fast
Model names, aliases, context windows, pricing, tool support, and recommended usage can change quickly. Official docs and model catalogs are the first place to check before relying on a course, tweet, or old benchmark.
Bookmark OpenAI, Anthropic, Gemini, Llama, Mistral, Cohere, DeepSeek, Qwen, xAI, Perplexity, Together, and OpenRouter docs if your work depends on model behavior. Today that means checking pages that already show the current families and migrations: GPT-5.5 plus the all-models catalog in OpenAI's docs, Anthropic's Claude overview plus release notes, Gemini model tables plus Interactions, 3.5 Flash guidance, and deprecations in Google's docs, Devstral, Magistral, and Voxtral updates in Mistral's docs, and OpenRouter's large model directory for cross-provider discovery. Secondary summaries are useful only after the primary source is checked.
Use catalogs to narrow the test set
A model catalog should not make the final decision for you. It should narrow the candidates by modality, context, tool support, speed, price, hosting route, and data constraints.
Once the list is short, test the models on your own prompts and workflows. That is the only way to know whether a model is right for your coding agent, RAG system, product feature, or research assistant.
Recommended courses and resources
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The AI Engineer Roadmap
Free tutorial · Matt Pocock · Beginner to intermediate
You want a guided path through core AI concepts, model selection, the AI engineering mindset, evals, and techniques for improving LLM-powered apps.
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Model Context Protocol Tutorial
Free tutorial · Matt Pocock · Intermediate
You want to understand MCP and build TypeScript MCP servers over stdio or HTTP, connect Claude Code to tools, use MCP prompts, and package servers for distribution.
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AI Coding Dictionary
Dictionary · Matt Pocock · Beginner to intermediate
You want plain-English definitions for agentic coding concepts such as context windows, tools, MCP, handoffs, skills, subagents, feedback loops, and agent-ready work.
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MCP: Build Rich-Context AI Apps with Anthropic
Short course · DeepLearning.AI · Intermediate
You want a hands-on MCP course for connecting tools, context, and Claude-powered apps.
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Hugging Face MCP Course
Free course · Hugging Face · Beginner to intermediate
You want a free structured MCP path with concepts, assignments, SDKs, and a certificate route.
