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Custom pricing for production workloads
Meta's open-weight coding model, trained for agentic coding workflows with reliable tool calling and deep codebase understanding, with a 1M-token context window.
Send text, images, video, audio or PDF documents to the chat completions endpoint and receive text.
meta/muse-spark-openhttps://api.aimlapi.com/v1Muse Spark Open is an open-weight model from Meta optimized for coding. It is trained for agentic coding workflows, in which a model reads a codebase, calls tools and acts on what they return, and Meta positions it on two properties: reliable tool calling and deep codebase understanding. It was released on 15 October 2026 and is available on AI/ML API as meta/muse-spark-open.
The model belongs to Meta's Muse Spark line. Muse Spark 1.3 is the general-purpose API model of that line, and Muse Wonder is Meta's frontier agentic model; Muse Spark Open is the variant published with open weights and tuned for coding.
The model targets coding agents rather than single-turn code completion. A 1M-token context window lets an agent keep a large part of a repository, its tool outputs and the conversation history in one request, and reasoning tokens let the model plan a change before it makes it. Image, video and PDF input cover the material that sits around code: screenshots of a UI, recordings of a bug, design documents and specifications.
Coding agents that work across a repository: reading the relevant files, calling tools such as a shell, a test runner or a search index, and editing code based on what those tools return.
Large-context code tasks, such as reviewing a long diff, answering questions about an unfamiliar codebase or planning a refactor with most of the project in the prompt.
Debugging from mixed inputs, where the model gets a stack trace together with a screenshot, a screen recording or a PDF specification of the expected behaviour.
Teams that want an open-weight Meta model for coding work and use the hosted endpoint for development or overflow capacity.
See how Muse Spark Open compares in intelligence, generation speed, cost per task and latency. Data retrieved Nov 3, 2026.
Compare 6 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Agentic tasks completed in a command-line environment.
Reasoning across long documents of about 100k tokens.
Python coding tasks drawn from scientific research.
| Usage | Price | Unit |
|---|---|---|
| Input | $1.32 | 1M tokens |
| Output | $4.5 | 1M tokens |
| Cached input | $0.1587 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $1.32 | $4.5 | |
| $0.3703 | $1.59 | |
| $1.32 | $4.5 | |
| $1.32 | $4.5 | |
| $4.23 | $21.16 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "meta/muse-spark-open",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "meta/muse-spark-open",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "meta/muse-spark-open",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Yes. Muse Spark Open is live on AI/ML API as meta/muse-spark-open, through the chat completions endpoint.
Muse Spark Open has a 1,048,576-token context window and can return up to 943,718 tokens.
It accepts text, images, video, audio and PDF documents and returns text. Reasoning, tool calling and streaming are supported.
Muse Spark Open is the open-weight member of Meta's Muse Spark line and is trained specifically for agentic coding, with an emphasis on tool calling and codebase understanding. Muse Spark 1.3 is the general-purpose model of the line. On AI/ML API both are billed at the same per-token rate.
Muse Spark Open costs $1.32 input and $4.5 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Meta trained it for agentic coding: multi-step work in which the model reads a codebase, calls tools and edits code based on the results. Its 1M-token context window fits a large part of a repository in a single request.
Use meta/muse-spark-open as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.