How Models Actually Works
Continuing to train a base model on your own examples to specialize its behavior. This guide pulls together everything on Onexial tagged models — 7 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Models
Before wiring anything together, the vocabulary has to be precise. These 3 definitions cover the terms that show up in almost every Models discussion — each one links to a full entry with an example and its own connections inside the graph.
Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
Context Window
The maximum amount of text (in tokens) an LLM can consider in a single call.
Multimodal AI
Models that natively process more than one input type — text, images, audio, or video.
Trade-offs and comparisons
Most Models decisions are trade-offs rather than right answers. These 4 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.
OpenAI vs Gemini for Agent Building
Both ship strong models and SDKs — differences are in tool calling, context and pricing.
Claude vs GPT vs Gemini
The three frontier model families, side by side.
Claude Sonnet vs Opus
When to spend up within the Claude family.