Meta frontier reasoning model with a 1M token context, image and video understanding, built-in web search with cited sources, and tool calling.
Meta AIText Generation1M contextProprietary EndpointNewMeta frontier reasoning model with a 1M token context, image and video understanding, built-in web search with cited sources, and tool calling.
Also known as Muse Spark, Meta AI Muse Spark 1.1, Muse-Spark-1.1, muse-spark-1-1
muse-spark-1-1/v1/chat/completionsPOST/v1/responsesPOST/v1/messagesPOST/v1beta/models/muse-spark-1-1:generateContentLive pay-as-you-go rates from the EmpirioLabs catalog. You are billed only for what you use, with no monthly minimum.
Muse Spark 1.1 serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id muse-spark-1-1. Get an API key from the EmpirioLabs dashboard.
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": "Write a haiku about the ocean."}
]
}'from openai import OpenAI
client = OpenAI(
base_url="https://api.empiriolabs.ai/v1",
api_key="YOUR_EMPIRIOLABS_API_KEY",
)
response = client.chat.completions.create(
model="muse-spark-1-1",
messages=[{"role": "user", "content": "Write a haiku about the ocean."}],
)
print(response.choices[0].message.content)Request parameters supported by the Muse Spark 1.1 API on EmpirioLabs. Defaults apply when a field is omitted.
| Parameter | Type | Default | Range / values | Description |
|---|---|---|---|---|
| max_tokens | integer | 16384 | 1 to 131072 | Maximum number of output tokens to generate. Reasoning tokens count against this budget. |
| temperature | number | 1 | 0 to 2 | Controls randomness. Lower values make responses more deterministic. |
| top_p | number | 1 | 0.01 to 1 | Nucleus sampling cutoff. |
| presence_penalty | number | 0 | -2 to 2 | Penalizes tokens that already appeared, encouraging new topics. |
| frequency_penalty | number | 0 | -2 to 2 | Penalizes frequent tokens, reducing repetition. |
| seed | integer | - | - | Random seed for more reproducible sampling. |
| reasoning_effort | enum | medium | minimal, low, medium, high, xhigh | 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... |
| tool_web_search | boolean | 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 | [] | - | OpenAI-compatible function calling tool definitions. |
| tool_choice | object | - | - | OpenAI-compatible tool choice control. This model supports auto and none. |
| response_format | enum | - | - | Return structured JSON output. JSON mode returns any valid JSON object; JSON Schema mode enforces an exact schema. |
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.
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:
"usage": {
"prompt_tokens": 123,
"completion_tokens": 456,
"cost_usd": 0.0042,
"tool_usage": {"web_search": 3, "code_interpreter": 1}
}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.
On EmpirioLabs, Muse Spark 1.1 is billed pay as you go: Input $1.25 per 1M prompt tokens; Output $4.25 per 1M generated tokens; Implicit cache read $1.00 per 1M cached input tokens. The live rate card on this page always matches what the API charges.
Muse Spark 1.1 supports a 1M-token context window with up to 131,072 output tokens per response.
Yes. Muse Spark 1.1 serves the OpenAI-compatible Chat Completions API, so existing OpenAI SDKs work by pointing base_url at https://api.empiriolabs.ai/v1 and setting the model id to muse-spark-1-1.
Yes. The EmpirioLabs playground runs Muse Spark 1.1 in the browser with the same parameters the API exposes, so you can test prompts before writing code.
Create an EmpirioLabs account, then generate a key under API Keys in the dashboard. Billing is pay-as-you-go credits, so you only pay for the requests you make.
Check out our pricing or reach out if you want your own model deployed on our stack.
