Model

Key facts

• It is the raw computational substrate of every AI system: a frozen artifact produced by pretraining on trillions of tokens, often further shaped by instruction tuning and alignment.
• Examples include Claude Opus 4.7 and Sonnet 4.6 from Anthropic, GPT-5 from OpenAI, and Google's Gemini family.
• On its own a model is stateless and narrow.
• Given input tokens it predicts the next token distribution — that is the whole job.
• It has no memory between API calls, no filesystem, no ability to browse the web, no "session." Everything else users associate with AI — conversation history, tool access, long-horizon behavior, retrieval — is supplied by the layers wrapping it.
• When choosing a model you typically trade off capability (frontier reasoning, coding, math), context length, latency and price per million tokens, and modality support (text, vision, audio, video).
• Post-training recipe, training cutoff, and parameter count all shape real-world behavior even when the architecture looks similar.
• In our five-layer mental model — model → tools → context → harness → agent — the model is the primitive that every other layer wraps.
• Swapping the underlying model (say, Sonnet 4.6 for Opus 4.7 inside the same harness) is one of the most common agentic-system upgrades.
• As a storefront-level anchor, $1.10 / $4.40 per 1M tokens is the May 2026 standard-rate pair we track on the canonical OpenAI API routing row for o4-mini (always re-check routing before estimating spend).
• See the models directory for the full catalog with pricing, context windows, and benchmark results.

References & related pages

• $1.10 / $4.40 per 1M tokens
• models directory

Models Mentioning Model(12)