Skip to main content
timbal/auto* models are served by the Timbal platform: there is no vendor key for them, and they need a platform subject (a deployed app, or TIMBAL_API_KEY with an app).
Set model="timbal/auto" and the Timbal platform picks the model and reasoning effort for each turn, from the conversation and the attachments on the latest user message: a quick language turn, tool or retrieval work, or long reasoning each land on a different tier. Nothing else changes: no configuration and no new API. Routing adds roughly 250 ms once per turn and a routing fee of about $0.0001 per turn next to the served model’s own usage.

​
Models

auto

timbal/autoBalanced. Alias: timbal/auto-balanced.
  • Claude Haiku 5.5, Sonnet 5.5 and Opus 5.5 by tier
  • GPT-6 Luna for long-text digests
  • 500K context

auto-cost

timbal/auto-costHaiku 5.5 for fast turns, Sonnet 5.5 for the rest, GPT-6 Luna for long-text digests; Astra specialists off.
  • 500K context

auto-intelligence

timbal/auto-intelligenceOpus 5.5 at every tier, with effort rising by tier; specialists on.
  • 1M context

​
Tiers and fallbacks

The three models share the same router and differ in which models, and at which reasoning effort, fill the tiers. Each tier carries ordered fallbacks: if a model answers 429/5xx before streaming, is unreachable, or is disabled by the organization’s model policies, the request moves to the next one, cross-vendor where possible. Fast turns with a prompt above about 100K tokens go to GPT-6 Luna first, because Haiku 5.5 bills those at 5× its base rate. In timbal/auto and timbal/auto-cost, two kinds of turn skip the medium tier:
  • Long-text digests: summarizing, extracting from, translating or answering questions about more than about 30K tokens the user provided (pasted text or attached pages) go to GPT-6 Luna · medium → Haiku 5.5 → Gemini 3.5 Flash-Lite instead of Sonnet 5.5. Analysis of the same text, or building a file from it, stays on the tiers above.
  • Recordings: transcribing or summarizing an audio or video attachment goes to Gemini 3.5 Flash-Lite → Gemini 3.8 Flash.
Other audio or video work goes to Gemini 3.8 Flash → Gemini 3.5 Flash-Lite in every model; only Google takes audio and video natively, so those fallbacks stay on Google. 3D or CAD as code and hard math or science go to GPT-6 Astra (timbal/auto and timbal/auto-intelligence only). A request that already sets reasoning_effort keeps it on every model, mapped to the nearest level that model accepts. timbal/auto-intelligence sends every turn, small talk included, to a frontier model; that is the point of the profile. Why these models: on GDPval-AA (documents, spreadsheets and slides judged by professionals) Opus 5.5 and Sonnet 5.5 lead, Haiku 5.5 outscores GPT-6 Astra, and effort level moves scores more than model choice. Astra leads 3D-as-code, CAD and frontier math; Grok 4.7 is the best-calibrated non-Anthropic model for deliverables. GPT-6 Luna keeps its base price up to 272K tokens and is close to Opus 5.5 on long-context reasoning, which makes it the cheap reader for long documents. Gemini 3.5 Flash-Lite is the cheapest model with native audio and video input and the third vendor behind the fast tier. The maps are platform configuration and are re-checked against public leaderboards monthly.

​
How it behaves in the SDK

  • Endpoint: always the platform chat-completions proxy, whatever TIMBAL_OPENAI_API is set to, because that is the only request shape the proxy can re-target across providers.
  • Attachments: the SDK names the latest turn’s attachments in metadata.timbal_attachments; the platform removes timbal_* keys before the request reaches a provider.
  • One model per turn: the model is chosen once per turn, so every call of an agent’s tool loop uses the same model.
  • Usage and traces: recorded under the model that served the turn (for example anthropic/claude-haiku-5-5), not timbal/auto.
  • Context window: each entry declares the smallest window among the models it can route to, so memory compaction and attachment bounding stay safe whichever model serves.