OpenInference JS
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    LLM_COST: "llm.cost" = ...

    Key prefix for cost information. When these keys are transformed into a JSON-like structure, it would look like: { "prompt": 0.0021, # Cost in USD "completion": 0.0045, # Cost in USD "total": 0.0066, # Cost in USD "completion_details": { "output": 0.0009, # Cost in USD "reasoning": 0.0024, # Cost in USD (e.g., 80 tokens * $0.03/1K tokens) "audio": 0.0012 # Cost in USD (e.g., 40 tokens * $0.03/1K tokens) }, "prompt_details": { "input": 0.0003, # Cost in USD "cache_write": 0.0006, # Cost in USD (e.g., 20 tokens * $0.03/1K tokens) "cache_read": 0.0003, # Cost in USD (e.g., 10 tokens * $0.03/1K tokens) "cache_input": 0.0006, # Cost in USD (e.g., 20 tokens * $0.03/1K tokens) "audio": 0.0003 # Cost in USD (e.g., 10 tokens * $0.03/1K tokens) } } Note: This is a key prefix - individual attributes are stored as separate span attributes with this prefix, e.g. llm.cost.prompt, llm.cost.completion_details.reasoning, etc. The JSON structure shown above represents how these separate attributes can be conceptually organized. All monetary values are in USD with floating point precision.