Create Completion

The name of the model to use.

Example: "accounts/fireworks/models/kimi-k2-instruct-0905"

A unique identifier representing your end-user, which can help monitor and detect abuse.

A key used for prompt caching session affinity. Requests with the same prompt_cache_key are routed to the same backend to maximize KV cache hit rates. This is the preferred field for session affinity (takes priority over the 'user' field).

Isolation key for prompt caching to separate cache entries.

Return raw output from the model.

Whether to include performance metrics in the response body.

Non-streaming requests: Performance metrics are always included in response headers (e.g., fireworks-prompt-tokens, fireworks-server-time-to-first-token). Setting this to true additionally includes the same metrics in the response body under the perf_metrics field.

Streaming requests: Performance metrics are only included in the response body under the perf_metrics field in the final chunk (when finish_reason is set). This is because headers may not be accessible during streaming.

The response body perf_metrics field contains the following metrics:

Basic Metrics (all deployments):

• prompt-tokens: Number of tokens in the prompt
• cached-prompt-tokens: Number of cached prompt tokens
• server-time-to-first-token: Time from request start to first token (in seconds)
• server-processing-time: Total processing time (in seconds, only for completed requests)

Predicted Outputs Metrics:

• speculation-prompt-tokens: Number of speculative prompt tokens
• speculation-prompt-matched-tokens: Number of matched speculative prompt tokens (for completed requests)

Dedicated Deployment Only Metrics:

• speculation-generated-tokens: Number of speculative generated tokens (for completed requests)
• speculation-acceptance: Speculation acceptance rates by position
• backend-host: Hostname of the backend server
• num-concurrent-requests: Number of concurrent requests
• deployment: Deployment name
• tokenizer-queue-duration: Time spent in tokenizer queue
• tokenizer-duration: Time spent in tokenizer
• prefill-queue-duration: Time spent in prefill queue
• prefill-duration: Time spent in prefill
• generation-queue-duration: Time spent in generation queue
• generation-duration: Time spent in generation

Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

How many completions to generate for each prompt.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

Required range: 1 <= x <= 128

Example: 1

The service tier to use for the request. Specifies the processing type used for serving the request. Only "priority" is supported, while all other values will be treated as "default" tier.

The maximum number of tokens to generate in the completion. If the token count of your prompt plus max_tokens exceeds the model's context length, the behavior depends on context_length_exceeded_behavior. By default, max_tokens will be lowered to fit in the context window instead of returning an error.

Alias for max_tokens. Cannot be specified together with max_tokens.

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

Required range: 0 <= x <= 2

Example: 1

Top-k sampling is another sampling method where the k most probable next tokens are filtered and the probability mass is redistributed among only those k next tokens. The value of k controls the number of candidates for the next token at each step during text generation. Must be between 0 and 100.

Required range: 0 <= x <= 100

Example: 50

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

Required range: 0 <= x <= 1

Example: 1

Minimum probability threshold for token selection. Only tokens with probability >= min_p are considered for selection. This is an alternative to top_p and top_k sampling.

Required range: 0 <= x <= 1

Typical-p sampling is an alternative to nucleus sampling. It considers the most typical tokens whose cumulative probability is at most typical_p.

Required range: 0 <= x <= 1

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

Reasonable value is around 0.1 to 1 if the aim is to just reduce repetitive samples somewhat. If the aim is to strongly suppress repetition, then one can increase the coefficients up to 2, but this can noticeably degrade the quality of samples. Negative values can be used to increase the likelihood of repetition.

See also presence_penalty for penalizing tokens that have at least one appearance at a fixed rate.

OpenAI compatible (follows OpenAI's conventions for handling token frequency and repetition penalties).

Required range: -2 <= x <= 2

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

Reasonable value is around 0.1 to 1 if the aim is to just reduce repetitive samples somewhat. If the aim is to strongly suppress repetition, then one can increase the coefficients up to 2, but this can noticeably degrade the quality of samples. Negative values can be used to increase the likelihood of repetition.

See also frequency_penalty for penalizing tokens at an increasing rate depending on how often they appear.

OpenAI compatible (follows OpenAI's conventions for handling token frequency and repetition penalties).

Required range: -2 <= x <= 2

Applies a penalty to repeated tokens to discourage or encourage repetition. A value of 1.0 means no penalty, allowing free repetition. Values above 1.0 penalize repetition, reducing the likelihood of repeating tokens. Values between 0.0 and 1.0 reward repetition, increasing the chance of repeated tokens. For a good balance, a value of 1.2 is often recommended. Note that the penalty is applied to both the generated output and the prompt in decoder-only models.

Required range: 0 <= x <= 2

Defines the target perplexity for the Mirostat algorithm. Perplexity measures the unpredictability of the generated text, with higher values encouraging more diverse and creative outputs, while lower values prioritize predictability and coherence. The algorithm dynamically adjusts the token selection to maintain this target during text generation.

If not specified, Mirostat sampling is disabled.

Specifies the learning rate for the Mirostat sampling algorithm, which controls how quickly the model adjusts its token distribution to maintain the target perplexity. A smaller value slows down the adjustments, leading to more stable but gradual shifts, while higher values speed up corrections at the cost of potential instability.

Random seed for deterministic sampling.

An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. The minimum value is 0 and the maximum value is 5.

When logprobs is set, top_logprobs can be used to modify how many top log probabilities are returned. If top_logprobs is not set, the API will return up to logprobs tokens per position.

Required range: 0 <= x <= 5

Echo back the prompt in addition to the completion.

Echo back the last N tokens of the prompt in addition to the completion. This is useful for obtaining logprobs of the prompt suffix but without transferring too much data. Passing echo_last=len(prompt) is the same as echo=True

This setting controls whether the model should ignore the End of Sequence (EOS) token. When set to True, the model will continue generating tokens even after the EOS token is produced. By default, it stops when the EOS token is reached.

What to do if the token count of prompt plus max_tokens exceeds the model's context window.

Passing truncate limits the max_tokens to at most context_window_length - prompt_length. This is the default.

Passing error would trigger a request error.

The default of 'truncate' is selected as it allows to ask for high max_tokens value while respecting the context window length without having to do client-side prompt tokenization.

Note, that it differs from OpenAI's behavior that matches that of error.

Controls how historical assistant reasoning content is included in the prompt for multi-turn conversations.

Accepted values:

• null: Use model/template default behavior (for GLM-4.7, the model/template default is 'interleaved', i.e. historical reasoning is cleared by default)
• 'disabled': Strip reasoning_content from all messages before prompt construction
• 'interleaved': Strip reasoning_content from messages up to (and including) the last user message
• 'preserved': Preserve historical reasoning_content across the conversation

Model support:

For other models, refer to the model provider's documentation.

Note: This parameter controls prompt formatting only. To disable reasoning computation entirely, use reasoning_effort='none'.

Return token IDs alongside text to avoid retokenization drift.

A unique identifier of the response

The object type, which is always "text_completion"

The Unix time in seconds when the response was generated

The model used for the completion