Every API here is available over the APIs.io API and to AI agents over MCP.
openapi: 3.2.0
info:
title: Fireworks AI Anthropic Compatible Messages anthropic-messages.openapi_other Text Completion.openapi Other API
description: Anthropic-compatible Messages API endpoint for Fireworks AI inference
version: 1.0.0
servers:
- url: https://api.fireworks.ai/inference
security:
- BearerAuth: []
tags:
- name: text-completion.openapi_other
x-displayName: other
paths:
/v1/completions:
servers:
- url: https://api.fireworks.ai/inference
post:
summary: Create 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_completion_v1_completions_post
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/CompletionRequest'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/CompletionResponse'
text/event-stream:
schema:
$ref: '#/components/schemas/CompletionStreamResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
tags:
- text-completion.openapi_other
security:
- BearerAuth: []
/v1/chat/completions:
servers:
- url: https://api.fireworks.ai/inference
post:
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'
tags:
- text-completion.openapi_other
security:
- BearerAuth: []
components:
schemas:
ChatCompletionResponseChoice:
additionalProperties: false
description: "A chat completion choice generated by a chat model.\n\nAttributes:\n index (int): The index of the chat completion choice.\n message (ChatMessage): The chat completion message.\n finish_reason (Optional[str]): The reason the model stopped generating tokens. This will be \"stop\" if\n the model hit a natural stop point or a provided stop sequence, or\n \"length\" if the maximum number of tokens specified in the request was\n reached.\n 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
NewLogProbs:
additionalProperties: false
description: OpenAI-compatible log probabilities format
properties:
content:
items:
$ref: '#/components/schemas/NewLogProbsContent'
title: Content
type: array
title: NewLogProbs
type: object
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.\n\n- `none`: the model will not call any tool and instead generates a message.\n- `auto`: the model can pick between generating a message or calling one or more tools.\n- `required` (alias: `any`): the model must call one or more tools.\n 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": <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
- items:
type: string
type: array
- 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:
- additionalProperties:
type: string
type: object
- type: 'null'
title: Metadata
description: Additional metadata to store with the request for tracing/distillation.
reasoning_effort:
anyOf:
- type: string
enum:
- low
- medium
- high
- 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 (only effective for grammar-based reasoning models).
**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''` maps to a default token limit (~3000 tokens).
- **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''`, and `''max''`. Default reasoning on (`''high''`). `''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 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": <int>}` - Enable thinking with a token budget (equivalent to `reasoning_effort: <int>`). Must be >= 1024.
- `{"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
RawOutput:
additiona
# --- truncated at 32 KB (85 KB total) ---
# Full source: https://raw.githubusercontent.com/api-evangelist/fireworks-ai/refs/heads/main/openapi/fireworks-ai-text-completion-openapi-other-api-openapi.yml