Muse Spark 1.1 | EmpirioLabs AI Docs

Meta AI · Text Generation

POST /v1/chat/completions

Meta frontier reasoning model with a 1M token context, image and video understanding, built-in web search with cited sources, and tool calling.

At a glance

Field	Value
Model id	`muse-spark-1-1`
Model release date	-
Input modalities	Text, Image, Video
Output modalities	Text
Context window	1M
Weight precision	-
Max output tokens	131,072
Features	reasoning, multimodal, function_calling, web_search, structured_output, cache
Native inference	No
New	Yes
Structured output	JSON Schema
Supported endpoints	`POST /v1/chat/completions`, `POST /v1/responses`, `POST /v1/messages`, `POST /v1beta/models/muse-spark-1-1:generateContent`

Pricing

Charge	Spec	Rate
Input	per 1M prompt tokens	$1.25
Output	per 1M generated tokens	$4.25
Implicit cache read	per 1M cached input tokens	$1.00
Web search	per search query	$0.00825

Example request

$ curl https://api.empiriolabs.ai/v1/chat/completions \
>   -H 'Authorization: Bearer $EMPIRIOLABS_API_KEY' \
>   -H 'Content-Type: application/json' \
>   -d '{"model": "muse-spark-1-1", "messages": [{"role":"user","content":"Hello"}]}'

Parameters

Parameter	Type	Required	Default	Description
`max_tokens`	integer	no	`16384`	Maximum number of output tokens to generate. Reasoning tokens count against this budget. · Range: 1 – 131072
`temperature`	number	no	`1`	Controls randomness. Lower values make responses more deterministic. · Range: 0 – 2
`top_p`	number	no	`1`	Nucleus sampling cutoff. · Range: 0.01 – 1
`presence_penalty`	number	no	`0`	Penalizes tokens that already appeared, encouraging new topics. · Range: -2 – 2
`frequency_penalty`	number	no	`0`	Penalizes frequent tokens, reducing repetition. · Range: -2 – 2
`seed`	integer	no	-	Random seed for more reproducible sampling.
`reasoning_effort`	enum	no	`"medium"`	Reasoning is always on; this sets how much effort the model spends before answering. Higher effort uses more reasoning tokens. Reasoning text is not returned in responses. · Allowed: `minimal`, `low`, `medium`, `high`, `xhigh`
`tool_web_search`	boolean	no	false	Enable built-in web search with cited sources. Adds $0.00825 per executed search query; a single request can run more than one search.
`tools`	array	no	`[]`	OpenAI-compatible function calling tool definitions.
`tool_choice`	object	no	-	OpenAI-compatible tool choice control. This model supports auto and none.
`response_format`	enum	no	-	Return structured JSON output. JSON mode returns any valid JSON object; JSON Schema mode enforces an exact schema.

Notes

Reasoning is always on and cannot be disabled. The model reasons internally and does not return its reasoning text; reasoning tokens bill as output tokens and count against max_tokens, so allow a generous output budget. reasoning_effort (minimal to xhigh) sets how hard the model thinks. Built-in web search adds $0.00825 per executed search query and cites its sources when available; a single request can run more than one search, and each executed query is billed and reported in usage.tool_usage. Supports image and video inputs, including together with web search. tool_choice supports auto and none.

Per-tool billing (usage.tool_usage)

When this model invokes tools (web search, code interpreter, etc.) inside a single request, the response carries a normalized usage.tool_usage map alongside the token counts. The example below shows the shape — exact field names, units, and which tools appear can vary slightly per provider:

1 "usage": {
2   "prompt_tokens": 123,
3   "completion_tokens": 456,
4   "cost_usd": 0.0042,
5   "tool_usage": {"web_search": 3, "code_interpreter": 1}
6 }

The tool counts are already factored into cost_usd — they are surfaced for transparency so you can audit per-tool billing. The field is omitted when no tools were invoked.

Machine-readable schema: GET https://api.empiriolabs.ai/v1/models/muse-spark-1-1.