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* fix(llms): send reasoning_effort to every openai reasoning model The completions path gated the parameter behind is_o1_model = "o1" in model.lower(), a literal substring test. gpt-5, o3 and o4-mini contain no "o1", so an explicitly configured effort was dropped and the model thought at the server default. The request still succeeded, so nothing surfaced -- one measured extraction ran 6.2s with the setting applied against 149.7s with it dropped. The gate could not be widened: is_o1_model also drives supports_function_calling, supports_stop_words and the system->user message rewrite, so marking gpt-5 as an o1 model would report that it cannot call tools. The parameter is forwarded unconditionally instead, matching the responses path, and a model that genuinely does not support it says so in a 400 that is retried once without the key. Also adds "minimal" to LLM.reasoning_effort, which gpt-5 accepts and the Literal omitted, so the cheapest setting was unreachable on the typed surface. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> * refactor(llms): gate reasoning_effort on model shape, not every model Forwarding to every model made a non-reasoning model pay a rejected request and a retry on every call. `_supports_reasoning_effort` matches on shape instead -- the o-series, and GPT generation 5 onwards -- so gpt-4o and gpt-4.1 never send the parameter at all. Matched by shape rather than by a list of names so a new member of an existing family works without a release here; gpt-6 and o5 already classify correctly. The unsupported-parameter retry stays as a safety net for the case the shape match is wrong for a future family, where it costs nothing when the match is right. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> * fix(llms): honour reasoning_effort on compatible servers and fine-tunes Review follow-ups. A fine-tune (ft:<base>:...) is judged by its base model. On an OpenAI-compatible server -- anything whose effective base URL is not api.openai.com, whether set explicitly, via env, or by a provider subclass -- the model name is the server's namespace and says nothing about support, so an explicit setting is sent as configured; a 400 naming the parameter, in whatever words the server uses, is recovered by retrying without it, unless it reads as a complaint about the value. A model that rejected the parameter is remembered per (endpoint, model) for the process so the rejected call is paid once, and the drop is logged as a warning since a configured setting is not being applied. The Literal also gains "xhigh", the remaining value the SDK accepts. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(llms): recover reasoning_effort only on evidence the parameter is unknown Two review follow-ups. The endpoint check now uses the same base URL precedence as the client itself, so a `client_params` override selects the server. And a rejection is recovered only when the message says the field is not one the server knows -- OpenAI's two shapes plus the common compatible-server wordings ("unknown field", "Extra inputs are not permitted") -- rather than any 400 that lacks a value-sounding word. A pydantic enum complaint such as "Input should be 'low', 'medium' or 'high'" names the parameter but is about its value, and surfaces instead of being dropped. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(llms): remember a reasoning_effort rejection only after the retry succeeds Two review points on the reasoning_effort fallback. The rejection was recorded before the retry ran, so a retry that died for an unrelated reason (a dropped connection, say) silently stopped sending the configured effort for the rest of the process even though a call without it had never succeeded; the (endpoint, model) is now remembered only once the retry returns. And _effective_base_url accepted only a str override in client_params while the SDK, and _get_client_params, accept httpx.URL too, so a compatible deployment configured with a URL object was detected as OpenAI and its rejection keyed under the wrong endpoint; both forms are normalised to the string the client calls. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Vidit Ostwal <110953813+Vidit-Ostwal@users.noreply.github.com>