# 💬 Responses

To an HTTP request, the API returns all results formatted in JSON.

## Response format​

The response is made of three parts:

• A request_id, uniquely identifying the request.
• An outputs list, containing the model answer to your request, and useful metadata.
• An execution_metadata dictionary, with the detailed cost of the request.

Let's have a detailed look at the request example generated on the Requests page.

Example response (JSON)
{    "request_id": "734e8d5a-a186-4d14-b2e4-be26f7fee6dc",    "outputs": [        [            {                "input_text": "Il était une fois",                "completions": [                    {                        "output_text": ", un pays où il faisait toujours beau.\nLes gens y vivaient heureux et en sécurité. Ils avaient de l'argent",                        "score": {                            "logprob": -42.8507080078125,                            "normalized_logprob": -1.7140283203125,                            "token_logprobs": null                        },                        "execution_metadata": {                            "cost": {                                "tokens_used": 29,                                "tokens_input": 4,                                "tokens_generated": 25,                                "cost_type": "lyra-fr@default",                                "batch_size": 1                            },                            "finish_reason": "length"                        }                    }                ]            }        ]    ],    "costs": {        "lyra-fr@default": {            "total_tokens_used": 29,            "total_tokens_input": 4,            "total_tokens_generated": 25,            "batch_size": 1        }    }}

## Common structures​

The execution_metadata dictionary collects information relevant to the cost and execution of the request. It is available at the top level, as well as for each individual element of a batch.

• It contains a cost entry, which is a dictionary containing the detailed total cost of the request:

• tokens_used, the number of tokens used (sum of the next two fields).
• tokens_input, the number of tokens sent in input to the model.
• tokens_generated, the number of tokens generated by the model.
• cost_type in the form model_name@skill, indicating the nature of the tokens used (if no skills are used, it will be replaced by default).
• batch_size the number of requests made in a single batch.
• And a finish_reason entry, explaining why the model stopped processing further tokens (length if stopped by n_tokens or by reaching the end of the text to process, or stop_word if reached one of the stop_words).

### Score​

The score dictionary provides information regarding the log-probabilities of the tokens processed:

• logprob is the overall log-probability of the entire text processed.
• normalized_logprob is the same as above, but normalized for text length (number of tokens).
• token_logprobs is a dictionary including the specific log-probability of each token.

## Response to batched requests​

The outputs list will be structured according to how you have batched your request:

• It will contain one separate list for each set of parameters you have submitted.
• Each list will contain one entry per entry in your batch.