Model aliases

aliases.json maps each model alias that doc pages use to a concrete model. This file covers choosing those models. How pages write aliases is in the "Model names" section of docs/AGENTS.md.

Chat model tiers

• Place a model in a tier by its price per token, on the scale of Anthropic's lineup: small is Haiku-priced, medium Sonnet-priced, large Opus-priced, and xlarge Fable-priced. Anthropic is the scale because it offers a model at all four prices.
• Compare output prices per token, by ratio: a model belongs to the tier whose Anthropic output price is the smallest multiple away. One price keeps placement unambiguous. Keep each provider's tiers in price order: a cheaper model never sits above a pricier one.
• Tier only general-purpose chat models. Coding, research, multi-agent, and other specialized models stay out of the tiers.
• Never rank a provider's models only against each other. Each provider's top model would then share one tier name across providers at very different prices.
• Read prices from the genai-prices package (genai_prices.data), which Pydantic AI already depends on.
• Give a tier the provider's newest model at that price.
• A newer model that matches an older one's capability at the same or lower price replaces it. Drop the older model from the lineup, then fill any tier it leaves with the gap rule below. Never keep both in neighboring tiers.
• Fill a tier that has no model at its price with the nearest model above it. At the top of the lineup, use the nearest model below. Every chat provider defines all four tiers, so a page can switch providers without checking which tiers exist.
• Mirror the anthropic: tiers in the bedrock: tiers, with the same Claude models.

Realtime, image, and embedding models

• Point <provider>:realtime, <provider>:image, and <provider>:embedding at the model the provider recommends for general use, not at its largest one. Readers copy examples as written.

Known models

• Point aliases at models in Pydantic AI's known model names. tests/test_docs_model_aliases.py checks each alias against them.
• A model that its provider serves but Pydantic AI does not list yet goes in that test's _NOT_YET_KNOWN. Remove it from _NOT_YET_KNOWN when a model PR adds it. The test fails until you do.

Current placements

Update this table whenever a change to aliases.json fills, opens, or moves a gap.