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models: Pareto role assignments from live catalogs; gpt-5.4-mini sidecar fallback#286

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models: Pareto role assignments from live catalogs; gpt-5.4-mini sidecar fallback#286
Zpankz wants to merge 1 commit into
1jehuang:masterfrom
Zpankz:models-pareto-role-assignment

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@Zpankz Zpankz commented May 31, 2026

Summary

Assigns Pareto-optimal models to each specialized agent role, derived from live provider catalogs queried via the wired OAuth credentials (not stale hardcoded lists).

  • Queried live catalogs: OpenAI/Codex backend, Antigravity cloudcode-pa (Ultra tier), xAI/Grok.
  • Added docs/model-role-assignment.md: per-role Pareto picks (primary coding, swarm, memory sidecar, autoreview, autojudge, ambient), config key mapping, a config example, catalog-drift notes, and reproducible fetch commands.
  • gpt-5.4-mini is now live (272k ctx, cheaper/faster than gpt-5.4); switched the OpenAI sidecar OAuth fallback to it and added it to ALL_OPENAI_MODELS.

Files

  • docs/model-role-assignment.md (new)
  • crates/jcode-base/src/sidecar.rsSIDECAR_OPENAI_OAUTH_FALLBACK_MODEL -> gpt-5.4-mini
  • crates/jcode-provider-core/src/models.rs — add gpt-5.4-mini to ALL_OPENAI_MODELS

Testing

  • cargo build --profile selfdev -p jcode-base -p jcode-provider-core clean (pre-existing warnings only).
  • 5 sidecar tests + 34 provider-core tests pass.

Notes

  • Role defaults remain None in config (provider-strongest behavior preserved); the doc lists the values to set for the Pareto pick per role.
  • codex-auto-review is visibility=hide; autoreview uses gpt-5.3-codex instead.

…sidecar fallback

Queried live provider catalogs via wired OAuth creds (Codex backend, Antigravity
cloudcode-pa, xAI) instead of relying on stale hardcoded lists. Documented
Pareto-optimal model->role assignments for every specialized agent role
(primary coding, swarm subagents, memory sidecar, autoreview, autojudge,
ambient) in docs/model-role-assignment.md, including a status table, a per-role
config example, and reproducible catalog-fetch commands.

gpt-5.4-mini is now live (272k ctx, cheaper/faster than gpt-5.4) so it is the
Pareto-better OpenAI sidecar fallback for the high-frequency relevance/extraction
workload. Added it to ALL_OPENAI_MODELS so the picker/catalog surface it.
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