Meta Model API

Property	Details
Description	Meta's Model API provides access to Meta's Muse Spark family of reasoning models.
Provider Route on LiteLLM	meta/
Supported Endpoints	/chat/completions, /responses
API Reference	Meta Model API Reference ↗

Required Variables

Environment Variables

os.environ["META_API_KEY"] = ""  # your Meta Model API key

Requests go to https://api.meta.ai/v1 by default. Set META_API_BASE to override the API base.

Supported Models

info

We actively maintain the list of models, pricing, token window, etc. here.

Model ID	Input context length	Input Modalities	Output Modalities
muse-spark-1.1	1M	Text, Image, Video, PDF	Text

muse-spark-1.1 supports function calling, parallel function calling, structured outputs, prompt caching, web search grounding, and reasoning via reasoning_effort ("minimal" through "xhigh").

Usage - LiteLLM Python SDK

Non-streaming

Meta Model API Non-streaming Completion

import os
import litellm
from litellm import completion

os.environ["META_API_KEY"] = ""  # your Meta Model API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

response = completion(model="meta/muse-spark-1.1", messages=messages)

Streaming

Meta Model API Streaming Completion

import os
import litellm
from litellm import completion

os.environ["META_API_KEY"] = ""  # your Meta Model API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

response = completion(
    model="meta/muse-spark-1.1",
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk)

Reasoning Effort

muse-spark-1.1 accepts reasoning_effort values "minimal", "low", "medium", "high", and "xhigh".

Meta Model API Reasoning Effort

import os
import litellm
from litellm import completion

os.environ["META_API_KEY"] = ""  # your Meta Model API key

messages = [{"content": "What is 15% of 2840?", "role": "user"}]

response = completion(
    model="meta/muse-spark-1.1",
    messages=messages,
    reasoning_effort="xhigh"
)

print(response.choices[0].message.content)
print(response.usage.completion_tokens_details.reasoning_tokens)

Function Calling

Meta Model API Function Calling

import os
import litellm
from litellm import completion

os.environ["META_API_KEY"] = ""  # your Meta Model API key

messages = [{"content": "What's the weather like in San Francisco?", "role": "user"}]

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = completion(
    model="meta/muse-spark-1.1",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.tool_calls)

Usage - LiteLLM Proxy

Add the following to your LiteLLM Proxy configuration file:

config.yaml

model_list:
  - model_name: muse-spark-1.1
    litellm_params:
      model: meta/muse-spark-1.1
      api_key: os.environ/META_API_KEY

Start your LiteLLM Proxy server:

Start LiteLLM Proxy

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

OpenAI SDK

Meta Model API via Proxy - Non-streaming

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

response = client.chat.completions.create(
    model="muse-spark-1.1",
    messages=[{"role": "user", "content": "Write a short poem about AI."}],
    reasoning_effort="minimal"
)

print(response.choices[0].message.content)

Meta Model API via Proxy - Streaming

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

response = client.chat.completions.create(
    model="muse-spark-1.1",
    messages=[{"role": "user", "content": "Write a short poem about AI."}],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

LiteLLM SDK

Meta Model API via Proxy - LiteLLM SDK

import litellm

response = litellm.completion(
    model="litellm_proxy/muse-spark-1.1",
    messages=[{"role": "user", "content": "Write a short poem about AI."}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key"
)

print(response.choices[0].message.content)

cURL

Meta Model API via Proxy - cURL

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{
    "model": "muse-spark-1.1",
    "messages": [{"role": "user", "content": "Write a short poem about AI."}],
    "reasoning_effort": "minimal"
  }'