> ## Documentation Index > Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create Chat Completion > Create a completion for the provided prompt and parameters. For RL / agent rollouts, Fireworks inference exposes additional rollout-specific features: [`x-session-affinity` and `x-multi-turn-session-id`](https://docs.fireworks.ai/guides/rollout-inference#session-affinity) for multi-turn trajectories, and [MoE Router Replay (R3)](https://docs.fireworks.ai/guides/rollout-inference#moe-router-replay) for MoE expert tracing during rollouts. ## OpenAPI ````yaml post /v1/chat/completions openapi: 3.1.0 info: title: Gateway REST API version: 4.259.0 servers: [] security: [] tags: - name: gateway.openapi_Gateway x-displayName: Gateway - name: gateway-extra.openapi_Gateway x-displayName: Gateway - name: responses.openapi_other x-displayName: other - name: text-completion.openapi_other x-displayName: other - name: anthropic-messages.openapi_other x-displayName: other paths: /v1/chat/completions: servers: - url: https://api.fireworks.ai/inference post: tags: - text-completion.openapi_other summary: Create Chat Completion description: >- Create a completion for the provided prompt and parameters. For RL / agent rollouts, Fireworks inference exposes additional rollout-specific features: [`x-session-affinity` and `x-multi-turn-session-id`](https://docs.fireworks.ai/guides/rollout-inference#session-affinity) for multi-turn trajectories, and [MoE Router Replay (R3)](https://docs.fireworks.ai/guides/rollout-inference#moe-router-replay) for MoE expert tracing during rollouts. operationId: create_chat_completion_v1_chat_completions_post requestBody: content: application/json: schema: $ref: '#/components/schemas/ChatCompletionRequest' required: true responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/ChatCompletionResponse' text/event-stream: schema: $ref: '#/components/schemas/ChatCompletionStreamResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - BearerAuth: [] components: schemas: ChatCompletionRequest: properties: model: type: string title: Model description: |- The name of the model to use. Example: `"accounts/fireworks/models/kimi-k2-instruct-0905"` messages: items: $ref: '#/components/schemas/ChatMessage' type: array title: Messages description: A list of messages comprising the conversation so far. tools: items: $ref: '#/components/schemas/ChatCompletionTool' type: array title: Tools description: >- A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. See the our [model library](https://app.fireworks.ai/models/?filter=LLM&functionCalling=true) for the list of supported models tool_choice: anyOf: - type: string enum: - auto - none - any - required - $ref: '#/components/schemas/FunctionSelection' title: Tool Choice description: >- Controls which (if any) tool is called by the model. - `none`: the model will not call any tool and instead generates a message. - `auto`: the model can pick between generating a message or calling one or more tools. - `required` (alias: `any`): the model must call one or more tools. To force a specific function, pass an object of the form `{ "type": "function", "name": "my_function" }` or `{ "type": "function", "function": { "name": "my_function" } }` for OpenAI compatibility. default: auto stream: anyOf: - type: boolean - type: 'null' title: Stream description: >- Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message. default: false response_format: anyOf: - $ref: '#/components/schemas/ResponseFormat' - type: 'null' description: >- Allows to force the model to produce specific output format. Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON. If `"type"` is `"json_schema"`, a JSON schema must be provided. E.g., `response_format = {"type": "json_schema", "json_schema": }`. Important: when using JSON mode, it's crucial to also instruct the model to produce JSON via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if `finish_reason="length"`, which indicates the generation exceeded `max_tokens` or the conversation exceeded the max context length. In this case the return value might not be a valid JSON. temperature: anyOf: - type: number - type: 'null' title: Temperature description: >- 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: anyOf: - type: integer - type: 'null' title: Top K description: >- 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` user: anyOf: - type: string - type: 'null' title: User description: >- A unique identifier representing your end-user, which can help monitor and detect abuse. prompt_cache_key: anyOf: - type: string - type: 'null' title: Prompt Cache Key description: >- 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). prompt_cache_isolation_key: anyOf: - type: string - type: 'null' title: Prompt Cache Isolation Key description: Isolation key for prompt caching to separate cache entries. raw_output: anyOf: - type: boolean - type: 'null' title: Raw Output description: Return raw output from the model. default: false perf_metrics_in_response: anyOf: - type: boolean - type: 'null' title: Perf Metrics In Response description: >- 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 default: false 'n': type: integer title: 'N' description: >- 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` default: 1 service_tier: type: string enum: - auto - default - flex - priority title: Service Tier description: >- 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. default: default stop: anyOf: - type: string - type: array items: type: string - type: 'null' title: Stop description: >- Up to 4 sequences where the API will stop generating further tokens. The returned text will NOT contain the stop sequence. max_tokens: anyOf: - type: integer - type: 'null' title: Max Tokens description: >- 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. max_completion_tokens: anyOf: - type: integer - type: 'null' title: Max Completion Tokens description: Alias for max_tokens. Cannot be specified together with max_tokens. top_p: anyOf: - type: number - type: 'null' title: Top P description: >- 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` min_p: anyOf: - type: number - type: 'null' title: Min P description: >- 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: anyOf: - type: number - type: 'null' title: Typical P description: >- 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` frequency_penalty: anyOf: - type: number - type: 'null' title: Frequency Penalty description: >- 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` presence_penalty: anyOf: - type: number - type: 'null' title: Presence Penalty description: >- 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` repetition_penalty: anyOf: - type: number - type: 'null' title: Repetition Penalty description: >- 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` mirostat_target: anyOf: - type: number - type: 'null' title: Mirostat Target description: >- 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. mirostat_lr: anyOf: - type: number - type: 'null' title: Mirostat Lr description: >- 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. seed: anyOf: - type: integer - type: 'null' title: Seed description: Random seed for deterministic sampling. logprobs: anyOf: - type: integer - type: boolean - type: 'null' title: Logprobs description: >- Include log probabilities in the response. This accepts either a boolean or an integer: If set to `true`, log probabilities are included and the number of alternatives can be controlled via `top_logprobs` (OpenAI-compatible behavior). If set to an integer N (0-5), include log probabilities for up to N most likely tokens per position in the legacy format. The API will always return the logprob of the sampled token, so there may be up to `logprobs+1` elements in the response when an integer is used. The maximum value for the integer form is 5. top_logprobs: anyOf: - type: integer - type: 'null' title: Top Logprobs description: >- 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: anyOf: - type: boolean - type: 'null' title: Echo description: Echo back the prompt in addition to the completion. default: false echo_last: anyOf: - type: integer - type: 'null' title: Echo Last description: >- 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` ignore_eos: type: boolean title: Ignore Eos description: >- 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. default: false context_length_exceeded_behavior: type: string enum: - error - truncate title: Context Length Exceeded Behavior description: >- 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`. default: truncate logit_bias: anyOf: - additionalProperties: type: number type: object - type: 'null' title: Logit Bias description: >- Modify the likelihood of specified tokens appearing in the completion. 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. speculation: anyOf: - type: string - items: type: integer type: array - type: 'null' title: Speculation description: Speculative decoding prompt or token IDs to speed up generation. prediction: anyOf: - $ref: '#/components/schemas/PredictedOutput' - type: string - type: 'null' title: Prediction description: >- OpenAI-compatible predicted output for speculative decoding. Can be a PredictedOutput object or a simple string. Automatically transformed to speculation. metadata: anyOf: - type: object additionalProperties: type: string - type: 'null' title: Metadata description: >- Additional metadata to store with the request for tracing/distillation. reasoning_effort: anyOf: - type: string enum: - low - medium - high - xhigh - max - none - type: integer - type: boolean - type: 'null' title: Reasoning Effort description: >- Controls reasoning behavior for supported models. When enabled, the model's reasoning appears in the `reasoning_content` field of the response, separate from the final answer in `content`. **Accepted values:** - **String** (OpenAI-compatible): `'low'`, `'medium'`, `'high'`, or `'max'` to enable reasoning with varying effort levels; `'none'` to disable reasoning. - **Boolean** (Fireworks extension): `true` to enable reasoning, `false` to disable it. - **Integer** (Fireworks extension): A positive integer to set a hard token limit on reasoning output. Integer values enable the model's normal medium-style thinking behavior and force the model to end its thinking phase after at most that many generated thinking tokens. **Important:** Boolean values are normalized internally: `true` becomes `'medium'`, and `false` becomes `'none'`. This normalization happens before model-specific validation, so if a model doesn't support `'none'`, passing `false` will produce an error referencing `'none'`. **Model-specific behavior:** - **Qwen3 (e.g., Qwen3-8B)**: Grammar-based reasoning. Default reasoning on. Use `'none'` or `false` to disable. Supports integer token limits to cap reasoning output. `'low'`, `'medium'`, and `'high'` keep their model-specific behavior and are not hard budgets. - **MiniMax M2**: Reasoning is required (always on). Defaults to `'medium'` when omitted. Accepts only string `reasoning_effort`: `'low'`, `'medium'`, or `'high'`. `'none'` and boolean values are rejected. - **DeepSeek V3.1**: Binary on/off reasoning. Default reasoning off (matches chat template). Use `true`, `'low'`, `'medium'`, or `'high'` to enable; `'none'` or `false` to disable. - **DeepSeek V3.2**: Binary on/off reasoning. Default reasoning on. Use `'none'` or `false` to disable; effort levels and integers have no additional effect. - **DeepSeek V4**: Accepts `'none'`, `'low'`, `'medium'`, `'high'`, `'xhigh'`, and `'max'`. Default reasoning on (`'high'`). `'xhigh'` is silently promoted to `'max'`. `'max'` prepends a thorough-reasoning preamble; `'high'` enables thinking. `'low'` and `'medium'` are silently promoted to `'high'`. `'none'` or `false` disables thinking. - **GLM 4.5, GLM 4.5 Air, GLM 4.6, GLM 4.7**: Binary on/off reasoning. Default reasoning on. Use `'none'` or `false` to disable; effort levels and integers have no additional effect. - **Harmony (OpenAI GPT-OSS 120B, GPT-OSS 20B)**: Accepts only `'low'`, `'medium'`, or `'high'`. Does not support `'none'`, `false`, or integer values — using these will return an error (e.g., "Invalid reasoning effort: none"). When omitted, defaults to `'medium'`. Lower effort produces faster responses with shorter reasoning. reasoning_history: anyOf: - type: string enum: - disabled - interleaved - preserved - type: 'null' title: Reasoning History description: >- 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:** | Model | Default | Supported values | | --- | --- | --- | | Kimi K2.6 | `'interleaved'` | `'disabled'`, `'interleaved'`, `'preserved'` | | Kimi K2 Instruct | `'preserved'` | `'disabled'`, `'interleaved'`, `'preserved'` | | MiniMax M2 | `'interleaved'` | `'disabled'`, `'interleaved'` | | GLM-4.7 | `'interleaved'` | `'disabled'`, `'interleaved'`, `'preserved'` | | GLM-4.6 | `'interleaved'` | `'disabled'`, `'interleaved'` | | Qwen 3.6 | `'preserved'` | `'disabled'`, `'preserved'` | | DeepSeek V4 | `'interleaved'` | `'interleaved'` | 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'`. thinking: anyOf: - $ref: '#/components/schemas/ThinkingConfigEnabled' - $ref: '#/components/schemas/ThinkingConfigDisabled' - type: 'null' title: Thinking description: >- Configuration for enabling extended thinking (Anthropic-compatible format). This is an alternative to `reasoning_effort` for controlling reasoning behavior. **Format:** - `{"type": "enabled"}` - Enable thinking (equivalent to `reasoning_effort: true`) - `{"type": "enabled", "budget_tokens": }` - Enable thinking with a token budget (equivalent to `reasoning_effort: `). Must be >= 1024. - `{"type": "enabled", "keep": "all"}` - Enable thinking and preserve all historical reasoning content in the prompt (equivalent to `reasoning_history: "preserved"`). - `{"type": "disabled"}` - Disable thinking (equivalent to `reasoning_effort: "none"`) **Note:** Cannot be specified together with `reasoning_effort`. If both are provided, a validation error will be raised. return_token_ids: anyOf: - type: boolean - type: 'null' title: Return Token Ids description: Return token IDs alongside text to avoid retokenization drift. default: false functions: items: $ref: '#/components/schemas/ChatCompletionFunction' type: array title: Functions description: >- Deprecated in OpenAI. Use 'tools' instead. This will be automatically transformed to tools. deprecated: true prompt_truncate_len: anyOf: - type: integer - type: 'null' title: Prompt Truncate Len description: >- The size (in tokens) to which to truncate chat prompts. This includes the system prompt (if any), previous user/assistant messages, and the current user message. Earlier user/assistant messages will be evicted first to fit the prompt into this length. The system prompt is preserved whenever possible and only truncated as a last resort. This should usually be set to a number much smaller << than the model's maximum context size, to allow enough remaining tokens for generating a response. If omitted, you may receive "prompt too long" errors in your responses as conversations grow. Note that even with this set, you may still receive "prompt too long" errors if individual messages (such as a very long system prompt or user message) exceed the model's context window on their own. parallel_tool_calls: anyOf: - type: boolean - type: 'null' title: Parallel Tool Calls description: Enable parallel function calling. safe_tokenization: anyOf: - type: boolean - type: 'null' title: Safe Tokenization description: >- When true, special tokens in user-provided content are never interpreted as actual special tokens during tokenization. This prevents prompt injection via special token strings (e.g. <|im_start|>, <｜User｜>). Supported for models using Jinja or HuggingFace chat templates with HuggingFace tokenizers. Returns an error if the model does not support it, or if combined with custom_chat_template on HuggingFace-backed models. Note: prompt_truncate_len is not applied when safe_tokenization is enabled. function_call: anyOf: - type: string enum: - auto - none - $ref: '#/components/schemas/FunctionNameSpec' - type: 'null' title: Function Call description: >- Deprecated in OpenAI. Use 'tool_choice' instead. This will be automatically transformed to tool_choice. deprecated: true additionalProperties: false type: object required: - messages - model title: ChatCompletionRequest ChatCompletionResponse: additionalProperties: false description: The response message from a /v1/chat/completions call. properties: id: description: A unique identifier of the response title: Id type: string object: default: chat.completion description: The object type, which is always "chat.completion" title: Object type: string created: description: The Unix time in seconds when the response was generated title: Created type: integer model: description: The model used for the chat completion title: Model type: string choices: description: The list of chat completion choices items: $ref: '#/components/schemas/ChatCompletionResponseChoice' title: Choices type: array usage: anyOf: - $ref: '#/components/schemas/UsageInfo' - type: 'null' default: null perf_metrics: anyOf: - additionalProperties: true type: object - type: 'null' default: null description: >- See parameter [perf_metrics_in_response](#body-perf-metrics-in-response) title: Perf Metrics prompt_token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null description: Token IDs for the prompt (when return_token_ids=true) title: Prompt Token Ids required: - id - created - model - choices title: ChatCompletionResponse type: object ChatCompletionStreamResponse: additionalProperties: false description: The streamed response message from a /v1/chat/completions call. properties: id: description: A unique identifier of the response title: Id type: string object: default: chat.completion.chunk description: The object type, which is always "chat.completion.chunk" title: Object type: string created: description: The Unix time in seconds when the response was generated title: Created type: integer model: description: The model used for the chat completion title: Model type: string choices: description: The list of streamed chat completion choices items: $ref: '#/components/schemas/ChatCompletionResponseStreamChoice' title: Choices type: array usage: anyOf: - $ref: '#/components/schemas/UsageInfo' - type: 'null' default: null perf_metrics: anyOf: - additionalProperties: true type: object - type: 'null' default: null description: >- See parameter [perf_metrics_in_response](#body-perf-metrics-in-response) title: Perf Metrics prompt_token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null description: >- Token IDs for the prompt (when return_token_ids=true, sent in first chunk) title: Prompt Token Ids required: - id - created - model - choices title: ChatCompletionStreamResponse type: object HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ChatMessage: properties: role: type: string title: Role description: >- The role of the messages author. One of `system`, `user`, or `assistant`. content: anyOf: - type: string - items: $ref: '#/components/schemas/ChatMessageContent' type: array - type: 'null' title: Content description: >- The contents of the message. `content` is required for all messages, and may be null for assistant messages with function calls. reasoning_content: anyOf: - type: string - type: 'null' title: Reasoning Content description: >- The reasoning or thinking process generated by the model. This field is only available for certain reasoning models (GLM 4.5, GLM 4.5 Air, GPT OSS 120B, GPT OSS 20B) and contains the model's internal reasoning that would otherwise appear in `` tags within the content field. tool_calls: anyOf: - items: $ref: '#/components/schemas/ChatCompletionMessageToolCall' type: array - type: 'null' title: Tool Calls description: The tool calls generated by the model, such as function calls. tool_call_id: anyOf: - type: string - type: 'null' title: Tool Call Id additionalProperties: false type: object required: - role title: ChatMessage description: A chat completion message. ChatCompletionTool: properties: type: type: string enum: - function title: Type description: The type of the tool. Currently, only `function` is supported. function: anyOf: - $ref: '#/components/schemas/ChatCompletionFunction' - type: 'null' description: Required for function tools. additionalProperties: false type: object required: - type title: ChatCompletionTool FunctionSelection: properties: type: type: string const: function title: Type function: anyOf: - $ref: '#/components/schemas/FunctionNameSpec' - type: 'null' additionalProperties: false type: object required: - type title: FunctionSelection ResponseFormat: properties: type: type: string enum: - json_object - json_schema - grammar - text title: Type schema: anyOf: - type: string - additionalProperties: true type: object - type: 'null' title: Schema grammar: anyOf: - type: string - type: 'null' title: Grammar json_schema: anyOf: - type: string - additionalProperties: true type: object - type: 'null' title: Json Schema additionalProperties: false type: object required: - type title: ResponseFormat PredictedOutput: properties: content: anyOf: - type: string - items: $ref: '#/components/schemas/ChatMessageContent' type: array title: Content type: type: string const: content title: Type default: content additionalProperties: false type: object required: - content title: PredictedOutput description: OpenAI-compatible struct for the "speculation" field. ThinkingConfigEnabled: properties: type: type: string const: enabled title: Type budget_tokens: anyOf: - type: integer - type: 'null' title: Budget Tokens description: >- Determines how many tokens the model can use for its internal reasoning process. Larger budgets can enable more thorough analysis for complex problems, improving response quality. Must be >= 1024 if specified. keep: anyOf: - type: string const: all - type: 'null' title: Keep description: >- Controls whether historical reasoning content is preserved in the prompt. When set to `"all"`, all previous assistant turns' reasoning_content is included in the rendered prompt (equivalent to `reasoning_history: "preserved"`). When omitted (null), the model uses its default history behavior. Only valid when `type` is `"enabled"`. budget_end_str: anyOf: - type: string - type: 'null' title: Budget End Str description: >- Natural-language transition phrase that the model is forced to emit just before the end-thinking token (``) when `budget_tokens` is exhausted. This produces a more natural conclusion than a hard token slam (matches vLLM's `reasoning_end_str` behavior). Defaults to a built-in phrase. Set to "" to disable the bridge and force `` immediately. Only meaningful when `budget_tokens` is set. additionalProperties: false type: object required: - type title: ThinkingConfigEnabled description: >- Configuration for enabling extended thinking (Anthropic-compatible format). ThinkingConfigDisabled: properties: type: type: string const: disabled title: Type additionalProperties: false type: object required: - type title: ThinkingConfigDisabled description: >- Configuration for disabling extended thinking (Anthropic-compatible format). ChatCompletionFunction: properties: name: type: string title: Name description: >- The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64. description: anyOf: - type: string - type: 'null' title: Description description: >- A description of what the function does, used by the model to choose when and how to call the function. parameters: additionalProperties: true type: object title: Parameters description: >- The parameters the function accepts, described as a JSON Schema object. The JSON Schema object should have the following structure: ```json { "type": "object", "required": ["param1", "param2"], "properties": { "param1": { "type": "string", "description": "..." }, "param2": { "type": "number", "description": "..." } } } ``` - The `type` field must be `"object"`. - The `required` field is an array of strings indicating which parameters are required. - The `properties` field is a map of property names to their definitions, where each property is an object with `type` (string) and `description` (string) fields. To describe a function that accepts no parameters, provide the value: ```json {"type": "object", "properties": {}} ``` strict: anyOf: - type: boolean - type: 'null' title: Strict additionalProperties: false type: object required: - name title: ChatCompletionFunction FunctionNameSpec: properties: name: type: string title: Name additionalProperties: false type: object required: - name title: FunctionNameSpec ChatCompletionResponseChoice: additionalProperties: false description: |- A chat completion choice generated by a chat model. Attributes: index (int): The index of the chat completion choice. message (ChatMessage): The chat completion message. finish_reason (Optional[str]): The reason the model stopped generating tokens. This will be "stop" if the model hit a natural stop point or a provided stop sequence, or "length" if the maximum number of tokens specified in the request was reached. token_ids (Optional[List[int]]): Token IDs for the generated message content (when return_token_ids=true) properties: index: title: Index type: integer message: $ref: '#/components/schemas/ChatMessage' finish_reason: anyOf: - type: string - type: 'null' default: null title: Finish Reason logprobs: anyOf: - $ref: '#/components/schemas/LogProbs' - $ref: '#/components/schemas/NewLogProbs' - type: 'null' default: null title: Logprobs raw_output: anyOf: - $ref: '#/components/schemas/RawOutput' - type: 'null' default: null token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null title: Token Ids required: - index - message title: ChatCompletionResponseChoice type: object UsageInfo: additionalProperties: false description: Usage statistics. properties: prompt_tokens: description: The number of tokens in the prompt title: Prompt Tokens type: integer total_tokens: description: The total number of tokens used in the request (prompt + completion) title: Total Tokens type: integer completion_tokens: anyOf: - type: integer - type: 'null' default: null description: The number of tokens in the generated completion title: Completion Tokens prompt_tokens_details: anyOf: - $ref: '#/components/schemas/PromptTokensDetails' - type: 'null' default: null description: Details about prompt tokens, including cached tokens required: - prompt_tokens - total_tokens title: UsageInfo type: object ChatCompletionResponseStreamChoice: additionalProperties: false description: A streamed chat completion choice. properties: index: description: The index of the chat completion choice title: Index type: integer delta: $ref: '#/components/schemas/DeltaMessage' description: The message delta finish_reason: anyOf: - enum: - stop - length - function_call - tool_calls type: string - type: 'null' default: null description: >- The reason the model stopped generating tokens. This will be "stop" if the model hit a natural stop point or a provided stop sequence, or "length" if the maximum number of tokens specified in the request was reached title: Finish Reason logprobs: anyOf: - $ref: '#/components/schemas/LogProbs' - $ref: '#/components/schemas/NewLogProbs' - type: 'null' default: null title: Logprobs raw_output: anyOf: - $ref: '#/components/schemas/RawOutput' - type: 'null' default: null prompt_token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null description: Token IDs for the prompt (when return_token_ids=true) title: Prompt Token Ids token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null description: Token IDs for this chunk (when return_token_ids=true) title: Token Ids required: - index - delta title: ChatCompletionResponseStreamChoice type: object ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type input: title: Input ctx: type: object title: Context type: object required: - loc - msg - type title: ValidationError ChatMessageContent: properties: type: type: string title: Type text: anyOf: - type: string - type: 'null' title: Text image_url: anyOf: - $ref: '#/components/schemas/ChatMessageContentImageURL' - type: 'null' video_url: anyOf: - $ref: '#/components/schemas/ChatMessageContentVideoURL' - type: 'null' additionalProperties: false type: object required: - type title: ChatMessageContent ChatCompletionMessageToolCall: properties: id: anyOf: - type: string - type: 'null' title: Id description: The ID of the tool call. type: type: string title: Type description: The type of the tool. Currently, only `function` is supported. default: function function: anyOf: - $ref: '#/components/schemas/ChatCompletionMessageToolCallFunction' - type: string title: Function description: The function that the model called. additionalProperties: false type: object required: - function title: ChatCompletionMessageToolCall LogProbs: additionalProperties: false description: Legacy log probabilities format properties: tokens: items: type: string title: Tokens type: array token_logprobs: items: type: number title: Token Logprobs type: array top_logprobs: anyOf: - items: additionalProperties: type: number type: object type: array - type: 'null' title: Top Logprobs text_offset: items: type: integer title: Text Offset type: array token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null title: Token Ids title: LogProbs type: object NewLogProbs: additionalProperties: false description: OpenAI-compatible log probabilities format properties: content: items: $ref: '#/components/schemas/NewLogProbsContent' title: Content type: array title: NewLogProbs type: object RawOutput: additionalProperties: false description: |- Extension of OpenAI that returns low-level interaction of what the model sees, including the formatted prompt and function calls properties: prompt_fragments: description: >- Pieces of the prompt (like individual messages) before truncation and concatenation. Depending on prompt_truncate_len some of the messages might be dropped. Contains a mix of strings to be tokenized and individual tokens (if dictated by the conversation template) items: anyOf: - type: string - type: integer title: Prompt Fragments type: array prompt_token_ids: description: Fully processed prompt as seen by the model items: type: integer title: Prompt Token Ids type: array completion: description: >- Raw completion produced by the model before any tool calls are parsed title: Completion type: string completion_token_ids: anyOf: - items: type: integer type: array - type: 'null' default: null description: Token IDs for the raw completion title: Completion Token Ids completion_logprobs: anyOf: - $ref: '#/components/schemas/NewLogProbs' - type: 'null' default: null description: >- Log probabilities for the completion. Only populated if logprobs is specified in the request images: anyOf: - type: array items: type: string - type: 'null' default: null description: Images in the prompt title: Images videos: anyOf: - type: array items: type: string - type: 'null' default: null description: Videos in the prompt title: Videos grammar: anyOf: - type: string - type: 'null' default: null description: >- Grammar used for constrained decoding, can be either user provided (directly or JSON schema) or inferred by the chat template title: Grammar required: - prompt_fragments - prompt_token_ids - completion title: RawOutput type: object PromptTokensDetails: additionalProperties: false properties: cached_tokens: anyOf: - type: integer - type: 'null' default: null title: Cached Tokens title: PromptTokensDetails type: object DeltaMessage: additionalProperties: false description: A message delta. properties: role: anyOf: - type: string - type: 'null' default: null description: The role of the author of this message title: Role content: anyOf: - type: string - type: 'null' default: null description: The contents of the chunk message title: Content reasoning_content: anyOf: - type: string - type: 'null' default: null description: >- The reasoning or thinking process generated by the model. This field is only available for certain reasoning models (GLM 4.5, GLM 4.5 Air, GPT OSS 120B, GPT OSS 20B) and contains the model's internal reasoning that would otherwise appear in `` tags within the content field. title: Reasoning Content tool_calls: anyOf: - items: $ref: '#/components/schemas/ChatCompletionMessageToolCall' type: array - type: 'null' default: null title: Tool Calls title: DeltaMessage type: object ChatMessageContentImageURL: properties: url: type: string title: Url detail: anyOf: - type: string - type: 'null' title: Detail additionalProperties: false type: object required: - url title: ChatMessageContentImageURL ChatMessageContentVideoURL: properties: url: type: string title: Url detail: anyOf: - type: string - type: 'null' title: Detail max_frames: anyOf: - type: integer - type: 'null' title: Max Frames spatial_limit: anyOf: - type: integer - type: 'null' title: Spatial Limit sample_fps: anyOf: - type: number - type: 'null' title: Sample Fps additionalProperties: false type: object required: - url title: ChatMessageContentVideoURL ChatCompletionMessageToolCallFunction: properties: name: anyOf: - type: string - type: 'null' title: Name description: The name of the function to call. arguments: anyOf: - type: string - additionalProperties: true type: object - type: 'null' title: Arguments description: >- The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function. additionalProperties: false type: object title: ChatCompletionMessageToolCallFunction NewLogProbsContent: additionalProperties: false properties: token: title: Token type: string logprob: title: Logprob type: number sampling_logprob: anyOf: - type: number - type: 'null' title: Sampling Logprob bytes: items: type: integer title: Bytes type: array top_logprobs: items: $ref: '#/components/schemas/NewLogProbsContentTopLogProbs' title: Top Logprobs type: array token_id: title: Token Id type: integer text_offset: title: Text Offset type: integer last_activation: anyOf: - type: string - type: 'null' default: null title: Last Activation routing_matrix: anyOf: - type: string - type: 'null' default: null title: Routing Matrix required: - token - logprob - sampling_logprob - bytes - token_id - text_offset title: NewLogProbsContent type: object NewLogProbsContentTopLogProbs: additionalProperties: false properties: token: title: Token type: string logprob: title: Logprob type: number token_id: title: Token Id type: integer bytes: items: type: integer title: Bytes type: array required: - token - logprob - token_id title: NewLogProbsContentTopLogProbs type: object securitySchemes: BearerAuth: type: http scheme: bearer description: >- Bearer authentication using your Fireworks API key. Format: Bearer bearerFormat: API_KEY ````