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* feat(ai): extract prompt cache token usage from OpenAI and Azure providers Parse the nested cache token details OpenAI returns and thread them into TokenUsage, matching the Anthropic and Bedrock providers. - sse.rs: add OpenAIPromptTokensDetails / OpenAIInputTokensDetails and the optional prompt_tokens_details / input_tokens_details fields. - other.rs (Chat Completions) and openai.rs (Responses): populate cache_read via .with_cache(cached_tokens, None). OpenAI's prompt_tokens/input_tokens already include cached tokens (cached is a subset), so total/prompt are unchanged; cache_read is recorded separately for reporting. For the same reason the frontend token-usage conversions are left as-is (adding cached would double-count); optional cache fields and a clarifying comment are added to prevent a future incorrect Anthropic-style fix. Fixes WIN-2207 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(ai): pin OpenAI/Azure cache-token deserialization paths Add regression tests deserializing the real Chat Completions and Responses usage payloads, guarding the prompt_tokens_details.cached_tokens / input_tokens_details.cached_tokens paths against a silent rename. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * refactor(ai): extract to_token_usage() and test the cache mapping Address review nit: move the usage->TokenUsage conversion into OpenAIChatUsage::to_token_usage / OpenAIResponsesUsage::to_token_usage so the providers call one method and the tests exercise the real mapping. Tests now assert cache_read is populated while input/total are unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>