Request Headers
The authorization token (required).
Request Path
The unique identifier of the completion to retrieve
Response Body
The original request body
Properties
A list of messages comprising the conversation so far. Depending on the model you use, different message types (modalities) are supported, like text, images, and audio.
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Properties
Variants
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Properties
Model ID used to generate the response.
This setting aims to control the repetition of tokens based on how often they appear in the input. It tries to use less frequently those tokens that appear more in the input, proportional to how frequently they occur. Token penalty scales with the number of occurrences. Negative values will encourage token reuse.
Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned.
An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
Output types that you would like the model to generate. Most models are capable of generating text, which is the default.
Items
The type of item to generate.
How many chat completion choices to generate for each input message.
Whether to enable parallel tool calling during tool use.
The predicted output from the model.
This setting aims to control the presence of tokens in the output. It tries to encourage the model to use tokens that are less present in the input, proportional to their presence in the input. Token presence scales with the number of occurrences. Negative values will encourage more diverse token usage.
Constrains effort on reasoning for some reasoning models.
Variants
An object specifying the format that the model must output.
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Responses will have no specific format.
Properties
Responses will be JSON Objects.
Properties
Responses will adhere to the provided JSON Schema.
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Whether to strictly enforce the schema. If true, the model will only output properties defined in the schema. If false, the model may output additional properties.
The JSON Schema object defining the expected structure.
If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed for some models.
Specifies the processing type used for serving the request.
Variants
Stop generation immediately if the model encounters any token specified in the stop array.
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Items
If set to true, the model response data will be streamed to the client as it is generated using server-sent events.
Options for streaming response.
Properties
If set, an additional chunk will be streamed before the data: [DONE] message. The usage field on this chunk shows the token usage statistics for the entire request, as well as the cost, if requested.
This setting influences the variety in the model’s responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. At 0, the model always gives the same response for a given input.
Controls which (if any) tool is called by the model.
Variants
The model will not call any tools.
The model may choose to call a tool if it deems it necessary.
The model must call a tool.
The model must call the specified function.
Properties
Properties
A list of tools the model may call.
Items
Properties
Properties
The JSON Schema object defining the expected structure.
Whether to strictly enforce the schema. If true, the model will only output properties defined in the schema. If false, the model may output additional properties.
An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability.
This setting limits the model’s choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P. A lower value makes the model’s responses more predictable, while the default setting allows for a full range of token choices. Think of it like a dynamic Top-K.
This tool searches the web for relevant results to use in a response.
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Variants
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This sets the upper limit for the number of tokens the model can generate in response. It won’t produce more than this limit. The maximum value is the context length minus the prompt length.
Represents the minimum probability for a token to be considered, relative to the probability of the most likely token. (The value changes depending on the confidence level of the most probable token.) If your Min-P is set to 0.1, that means it will only allow for tokens that are at least 1/10th as probable as the best possible option.
OpenRouter plugins.
Items
May include additional fields as needed.
Properties
OpenRouter provider preferences.
Properties
List of provider slugs to try in order.
Items
Whether to allow backup providers when the primary is unavailable.
Only use providers that support all parameters in your request.
Control whether to use providers that may store data.
Variants
List of provider slugs to allow for this request.
Items
List of provider slugs to skip for this request.
Items
List of quantization levels to filter by.
Items
Sort providers by price or throughput.
OpenRouter reasoning configuration.
Properties
An upper bound for the number of tokens that can be generated for reasoning.
Constrains effort on reasoning for some reasoning models.
Variants
Whether reasoning is enabled for this request.
Helps to reduce the repetition of tokens from the input. A higher value makes the model less likely to repeat tokens, but too high a value can make the output less coherent (often with run-on sentences that lack small words). Token penalty scales based on original token’s probability.
Consider only the top tokens with “sufficiently high” probabilities based on the probability of the most likely token. Think of it like a dynamic Top-P. A lower Top-A value focuses the choices based on the highest probability token but with a narrower scope. A higher Top-A value does not necessarily affect the creativity of the output, but rather refines the filtering process based on the maximum probability.
This limits the model’s choice of tokens at each step, making it choose from a smaller set. A value of 1 means the model will always pick the most likely next token, leading to predictable results. By default this setting is disabled, making the model to consider all choices.
OpenRouter accounting configuration.
Properties
Whether to include Cost in the response usage.
Controls the verbosity and length of the model response. Lower values produce more concise responses, while higher values produce more detailed and comprehensive responses.
Variants
Fallback models. Will be tried in order if the first one fails.
Items
The completion response
Properties
A unique identifier for the chat completion.
An array of choices returned by the model.
Items
An object representing a single choice in the chat completion.
Properties
The message generated by the model for this choice.
Properties
The content of the message generated by the model.
The refusal information if the model refused to generate a message.
The role of the message, which is always assistant for model-generated messages.
The annotations added by the model in this message.
Items
Properties
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The end index of the citation in the message content.
The start index of the citation in the message content.
The title of the cited webpage.
The URL of the cited webpage.
The audio generated by the model in this message.
Properties
The tool calls made by the model in this delta.
Items
The tool call ID.
Properties
The name of the function being called.
The arguments passed to the function.
The reasoning text generated by the model in this message.
The images generated by the model in this message.
Items
Properties
Properties
The reason why the model finished generating the response.
Variants
The model finished generating because it reached a natural stopping point.
The model finished generating because it reached the maximum token limit.
The model finished generating because it made one or more tool calls.
The model finished generating because it triggered a content filter.
The model finished generating because an error occurred.
The index of the choice in the list of choices.
The log probabilities of the tokens in the delta.
Properties
An array of log probabilities for each token in the content.
Items
Properties
The token text.
The byte representation of the token.
Items
A byte in the token's byte representation.
The log probability of the token.
Items
Properties
The token text.
The byte representation of the token.
Items
A byte in the token's byte representation.
The log probability of the token.
An array of log probabilities for each token in the refusal.
Items
Properties
The token text.
The byte representation of the token.
Items
A byte in the token's byte representation.
The log probability of the token.
Items
Properties
The token text.
The byte representation of the token.
Items
A byte in the token's byte representation.
The log probability of the token.
The Unix timestamp (in seconds) when the chat completion was created.
The model used for the chat completion.
The service tier used for the chat completion.
Variants
A fingerprint representing the system configuration used for the chat completion.
An object containing token usage statistics for the chat completion.
Properties
The number of tokens generated in the completion.
The number of tokens in the input prompt.
The total number of tokens used (prompt + completion).
Properties
The number of audio tokens generated.
The number of reasoning tokens generated.
Properties
The number of audio tokens in the input prompt.
The number of cached tokens in the input prompt.
The cost incurred for this chat completion, in Credits.
Properties
The cost charged by the upstream LLM provider, in Credits.
The cost charged by the upstream LLM provider's own upstream LLM provider, in Credits.
The upstream (or upstream upstream) LLM provider used for the chat completion.