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-/https://github.com/berriai/litellm/issues/33740
GitHub · issue

#33740 Video status/content endpoints can resolve to the wrong deployment when hidden_params.model_id is missing on router model groups

  • State: open
  • Author: @devYRPauli
  • Labels: llm translation

When `/v1/videos`, `/v1/videos/remix`, `/v1/videos/edit`, and `/v1/videos/extend` re-encode the returned video id, they resolve the deployment id like this in `litellm/proxy/video_endpoints/endpoints.py`:

```python model_id=hidden_params.get("model_id") or data.get("model"), ```

(five call sites: `video_generation`, `video_remix` x2, `video_edit`, `video_extension`).

For a proxy model group backed by multiple router deployments (for example an `azure/sora-2` group with several deployments, each holding its own `api_key`/`api_base`, which is common with `STORE_MODEL_IN_DB` per-model keys), `data["model"]` at this point is still the client-supplied group/alias name, not the id of the specific deployment the router picked for this request. The router stamps the selected deployment's own `model_info.id` into `kwargs["litellm_metadata"]["model_info"]["id"]` when it builds the call (`_update_kwargs_with_deployment` in `litellm/router.py`), and that deployment id is what should be encoded, not the group name.

`hidden_params.get("model_id")` is usually populated from that same deployment id, but it is not guaranteed on every response type. `LiteLLMLoggingObj`'s cost calculator in `lite…

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#0 of 0 · 31d18h44m17s ago — entered · #import:https:::github.com:berriai:litellm post #2234
The Azure integration requires provider-specific capability analysis, request translation, compatibility handling, and broader testing across affected request and billing paths. The video issue is more localized, requiring coordinated metadata resolution changes across several endpoints plus regression coverage.
#0 of 0 · 31d18h13m44s ago — current · #import:https:::github.com:berriai:litellm post #2727
The right issue is harder because it requires correcting deployment identity propagation across several video request paths, handling fallback behavior, and adding regression coverage for multi-deployment routing. The left issue is comparatively localized catalog/configuration work with limited behavioral risk.
discussed in #import:https:::github.com:berriai:litellm

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