- TestCodexAliasFailoverMappingHonorsModelRouting/no_routing_rule: expect
["text","image"] instead of ["text"] — image input is resolved per-account
via isOpenAICodexImageInputModel, independent of routing rules
- TestCodexAliasFailoverMappingHonorsModelRouting/routing_rule: expect
effort levels to include "ultra" (added by #6718)
- Resolve conflict in openai_codex_models_service_test.go: keep both
GPT-6 Astra instructions template assertion (from this PR) and ultra
reasoning level + multi_agent fields (from #6718 on main)
- Fix compilation error on main: add missing `group *Group` parameter
to buildCodexModelsManifestForAccounts calls in
openai_codex_model_metadata_test.go (lines 24, 32) — caused by #6688
changing the function signature while #6718 added new callers with
the old signature
An OpenAI group that keeps a failover account for a shared public alias
produced a Codex model descriptor with no reasoning levels and text-only
input, which leaves the client's reasoning-effort slider with nothing to
select.
Two accounts explicitly mapping one alias to different upstream models
makes codexExplicitModelTargetsConflictForPlatform true. With no
upstream_model_metadata snapshot to intersect, groupCodexModelMetadata
short-circuits to {reasoningConflict, inputModalitiesConflict}, so
DefaultReasoningLevel is nil (the key vanishes under omitempty) and
SupportedReasoningLevels is an empty array. mergeMissingCodexModelFields
fills only missing-or-null fields, so an empty array is never repaired.
Failing closed is right when the alias target is genuinely ambiguous, but
a group that declares model_routing for the alias has already resolved
that ambiguity: the operator stated which accounts serve it and which is
primary. Treating that as a conflict rejects a deliberate failover setup.
Manifest side: when a routing rule names the alias, restrict capability
declaration to the routed accounts and skip the conflict override.
Capabilities then fall back to the same by-name derivation used for a
single account without a metadata snapshot. Groups with no rule, a
disabled rule, or a rule for another alias keep failing closed.
Scheduling side: model_routing was stored and read platform-agnostically
(admin_group.go, Group.GetRoutingAccountIDs) but four gates in
gateway_scheduling.go only consulted it for Anthropic, so an OpenAI
group's rules were accepted and displayed yet never honored. Without
this, the manifest would honor a rule that scheduling ignores. Both
gates now share one predicate, with the allowed platform set defined in
a single place.
Selection inside a routing list is unchanged: the list is a membership
set, and the winner is still decided by Priority, then load, then
LastUsedAt.
Tests: alias-failover manifest recovery plus the three negative cases,
the platform predicate table, and an OpenAI routed selection that must
beat a higher-priority unrouted account.
Account model mappings decide availability, but generated Codex catalogs previously inferred capabilities from local model-name tables. Compatible upstreams can expose unknown models such as OpenCode x-preview-f-free, so reasoning levels, image input, and context limits were missing or wrong.
Sync capability metadata from the account model endpoint, enrich incomplete lists from the Models.dev provider matching the account base URL, and persist only complete snapshots. Mixed groups consume the safe intersection across schedulable accounts. When IDs sync without complete metadata, return an explicit warning and preserve the previous snapshot.
Third-party Responses providers often omit /models. If that endpoint returns 404 or 405 and the account already has concrete model mappings, use those configured upstream IDs for capability enrichment. Authentication, rate-limit, server, and network failures remain hard errors, and metadata is never guessed across providers by model name alone.
Advertise a single none choice for non-reasoning models so current Codex can select them, then omit that catalog placeholder when forwarding to compatible Responses upstreams while preserving official OpenAI request semantics.
shaw