A fixed preference list goes stale at every retirement: when GPT-5.5
retired for ChatGPT sign-in on 2026-10-14, every model the shipped list
named was refused and Codex answered 503 until a release changed it. The
working model also sat in one slot for the whole process, so two sign-ins
on different plans overwrote each other's model and the vision gate read
whichever had answered last.
The client now reads the models the account is offered the way the Codex
CLI does (GET /backend-api/codex/models?client_version=..., same sign-in
as the responses call; shape from codex-rs/protocol/src/openai_models.rs)
and walks: CODEX_MODEL first; our preference order kept to what is
offered; the other offered models in the list's priority order, leaving
out hidden, specialist and frontier-tier entries; the rest of our order
last, so the walk never holds fewer models than before. With
CODEX_MODEL_FALLBACK_CHAIN set the order is the operator's and the list is
not read.
The list is a hint, never a gate. The read goes through safeFetch with a
5 s bound and a 4 MiB cap, is shared by concurrent requests, cached per
sign-in for an hour, and a failed read waits ten minutes. Any failure
leaves the static walk, one ai.codex.models_unreadable warning per sign-in
reports it, and each request that builds the walk annotates a pinned
codex.models.* shape.
The working model, refused models and the list are kept per stored
credential (a person's connection, or the operator-shared central one),
bounded by TTL and a 1000-entry LRU, with a digest of the account id so a
credential reconnected to another account starts empty. A model the list
offered that then answers model_not_found goes to the back of that
sign-in's line for six hours. When the list names a model's input
modalities, the Codex vision gate reads them; otherwise the model family
decides, as before.
Refs #1287
Own-plan turns get 165k tokens, 200 s and 16 rounds, operator turns 55k,
120 s and 8; the final-answer reserve, the concurrent-turn slot and the
step cap move with them.
Codex requests carry a per-conversation prompt_cache_key and a stable
session id, api.openai.com gets the same key, Anthropic usage counts
cache reads and writes in the gross, and local servers report cached
prompt tokens, so the turn budget discounts cached input on every
provider.
Marc-André Bombeck