Models: The Complete Guide
Continuing to train a base model on your own examples to specialize its behavior. Fine-Tuning adjusts a pretrained model's weights using a curated dataset so it follows a specific style, format, or task more reliably than prompting alone. 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.
Frequently asked questions
- What is Fine-Tuning?
- Fine-Tuning adjusts a pretrained model's weights using a curated dataset so it follows a specific style, format, or task more reliably than prompting alone. It is most valuable when you need consistent tone, strict output schemas, or to compress a long system prompt.
- What is an example of Fine-Tuning?
- An e-commerce brand fine-tunes a small model on 5,000 of their best support replies, then runs it for first-line triage — keeping voice and policy consistent without a 3,000-token system prompt.
- Why does Fine-Tuning matter for AI and automation?
- Continuing to train a base model on your own examples to specialize its behavior. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is Context Window?
- The Context Window is the hard limit on how much input + output a model can hold at once, measured in tokens. Larger windows allow longer documents and richer agent state, but cost more and can dilute attention — making retrieval and summarization still essential.
- What is an example of Context Window?
- A 1M-token model could read an entire codebase at once, but a focused 8K-token RAG pipeline answering one question is often cheaper, faster and more accurate.
- Why does Context Window matter for AI and automation?
- The maximum amount of text (in tokens) an LLM can consider in a single call. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is Multimodal AI?
- Multimodal AI refers to models trained to understand and generate across modalities. They can read a screenshot, describe a chart, transcribe audio, or watch a short video — enabling agents that act on what users actually see and say, not just on typed text.
- What is an example of Multimodal AI?
- A QA agent takes a screenshot of a broken UI, reads the error text in the image, locates the offending React component, and proposes a fix — all in one pass.