* feat(ui): show ai&'s name and logo in the dashboard
The Add Model form already lists ai& from its provider metadata, but the dashboard had no entry for it, so the provider showed no logo and model tables printed the raw aiand slug. Adds ai& to the dashboard provider list with its official mark, plated in dark mode so the red and black mark keep their colours, and the aiand/openai/gpt-oss-120b model placeholder
* fix(aiand): retire deepseek-v4-flash, glm-5.2 and kimi-k2.7-code
ai& retired these three models on 2026-10-27 and https://api.aiand.com/v1/models now lists ten. The rows keep their prices with a deprecation_date, so earlier spend still prices, and aiand/* stops listing them, the same way baseten handles retired rows. The other ten rows already match the live prices, context windows and output limits
* feat(aiand): add deepseek-v4.1-flash-1017
ai& launches deepseek-ai/deepseek-v4.1-flash-1017 on 2026-11-02. It is served on https://staging-api.aiand.com/v1/models with the same price, context window, output limit and capabilities as deepseek-v4.1-flash, so its row copies that one
* fix(aiand): retire deepseek-v4.1-flash
ai& retires deepseek-ai/deepseek-v4.1-flash on 2026-10-30, now that deepseek-v4.1-flash-1017 is live in production on https://api.aiand.com/v1/models with the same price, limits and capabilities. Its row keeps its prices with a deprecation_date, like the other three retired ai& rows
* fix(aiand): retire deepseek-v4.1-flash on 2026-11-06
https://api.aiand.com/v1/models now gives deepseek-v4.1-flash a deprecation_date of 2026-11-05T15:00Z, which is 2026-11-06 00:00 in Japan, so its row uses 2026-11-06 instead of 2026-10-30 and aiand/* keeps listing it until then. The wildcard test now leaves a model out only once its retirement date has passed
* test(aiand): derive retired models from the rows instead of pinning dates
The wildcard test now reads each row's deprecation_date, so it holds whichever dates ai& sets, and the test that only restated the JSON dates is gone
* fix(aiand): retire models on their UTC date while the proxy runs, and add muse-spark-open
The wildcard listing now checks each row's deprecation_date when it expands, against the UTC date the field
documents, so a model drops on its day without a restart. deepseek-v4.1-flash now carries 2026-11-05, the
UTC day ai& retires it, and meta/muse-spark-open gets its price row from ai&'s live /v1/models
* fix(aiand): hide retired models only from bundled listings, including the wildcard health check
* fix(aiand): share the UTC retirement check with Together AI and the health check sort
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(baseten): list current Model APIs instead of retired 2023 deployment IDs
`baseten/*` wildcard expansion and `/v1/models` read a hardcoded set of
three 2023 deployment IDs (Falcon 7B / WizardLM / MosaicML) that no longer
exist, while the model cost map already carries current Baseten entries.
- Populate `baseten_models` from the model cost map, like fireworks_ai.
- Add `BasetenConfig.get_models()` against the Model API `/v1/models`
endpoint and register it in `ProviderConfigManager.get_provider_model_info`,
so `check_provider_endpoint: true` returns the live catalog.
- Remove the retired deployment IDs from `constants.py`.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(baseten): mark retired Model APIs deprecated and skip them in listings
15 Baseten entries in the model cost map are no longer servable: the Model
API returns 410 "model version ... has been deprecated" (DeepSeek-V3.1
returns 400 deactivated), and none appear in `inference.baseten.co/v1/models`.
- Set `deprecation_date` on those entries (kept for spend tracking).
- Skip entries past their `deprecation_date` when building `baseten_models`,
so `baseten/*` expansion only lists models that still serve.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(baseten): assert the custom api_base model listing on the returned models
The custom api_base test only inspected the patched client's call args, which trips the
TQ002 mock-echo gate. Serve the custom URL with respx and assert on the models that come
back instead, so a wrong host or a broken parse both fail.
* fix(baseten): drop newly retired models on cost map reload and fake discovery at the HTTP boundary
A cost map refresh only ever added to the Baseten set, so a model that gained a past
deprecation_date after boot stayed listed under baseten/* until restart. Discard it on reload.
Tests now serve the catalog with respx instead of patching our own HTTP client, the
isinstance registration test is gone since the get_valid_models test already proves
discovery end to end, and new test locals carry Final.
---------
Co-authored-by: AlexKer <AlexKer@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Follow-up to #40152. Greptile flagged the two new reasoning_effort tests for
assigning config and result without Final. Annotates every local in the file
so it passes the LIT010 check cleanly
* fix(baseten): forward reasoning_effort to Baseten Model API
Baseten's Model API accepts a top-level reasoning_effort parameter and
validates it server-side (https://docs.baseten.co/inference/model-apis/reasoning),
but BasetenConfig.get_supported_openai_params did not list it, so LiteLLM
silently dropped it when drop_params=True and raised UnsupportedParamsError
otherwise.
Add reasoning_effort to the supported params so map_openai_params forwards
it verbatim, mirroring the hosted_vllm and deepseek providers. Baseten
returns a clear 400 for values outside a model's supported set and ignores
the parameter on non-reasoning models, so verbatim passthrough is safe for
the whole catalog.
[Claude_Code]
* fix(baseten): forward reasoning_effort to Baseten Model API
Baseten's Model API accepts a top-level reasoning_effort parameter and
validates it server-side (https://docs.baseten.co/inference/model-apis/reasoning),
but BasetenConfig.get_supported_openai_params did not list it, so LiteLLM
silently dropped it when drop_params=True and raised UnsupportedParamsError
otherwise.
Add reasoning_effort to the supported params so map_openai_params forwards
it verbatim, mirroring the hosted_vllm and deepseek providers. Baseten
returns a clear 400 for values outside a model's supported set and ignores
the parameter on non-reasoning models, so verbatim passthrough is safe for
the whole catalog.
---------
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
