Muse Spark Open

Coding-optimized open model for long-horizon agentic work with 1M context

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About model

Build with Muse Spark Open. See the license and usage policy.

Muse Spark Open is the coding-optimized open weights model from Meta, trained specifically for agentic coding workflows: higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repositories developers actually work in. Meta describes it as the most capable US open-weights model to date. Trained on real developer workflows, from multi-file refactors to debugging sessions and long-running tasks, it holds entire projects in a 1M-token context window, including complex dependencies, legacy code, and thousands of files. Built from the ground up as an agentic model, it plans, acts, observes, and self-corrects over long horizons, browsing the web with multimodal browsing, spawning and coordinating subagents, and using computer-use capabilities unified across desktop, mobile, and browser. As a reasoning model with audio and audio-video perception alongside image and document understanding, it works across what it sees, hears, and reads. Available on Together AI.

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Context Window

Entire projects with complex dependencies, legacy code, and thousands of files

Multimodal Perception

Native

Works across what it sees, hears, and reads: images, documents, audio, and video

Intent Commentary

Built-In

States what it is doing and why before acting on long-horizon tasks

Model key capabilities

Agentic Coding: Trained on real developer workflows, from multi-file refactors to debugging sessions and long-running tasks, for higher first-attempt accuracy and reliable tool calling
Long-Horizon Autonomy: Plans, acts, observes, and self-corrects over long horizons, coordinating subagents for decomposable work and stating intent in separate commentary before acting
Multimodal Perception: Audio and audio-video perception alongside image and document understanding, reasoning across what it sees, hears, and reads
Production-Ready Infrastructure: 99.9% SLA, available on serverless and dedicated infrastructure

API usage

cURL
Python
Typescript

Endpoint:

meta-models/Muse-Spark-Open-816B-A42B

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-models/Muse-Spark-Open-816B-A42B",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'

from together import Together

client = Together()

response = client.chat.completions.create(
  model="meta-models/Muse-Spark-Open-816B-A42B",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)

import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'meta-models/Muse-Spark-Open-816B-A42B',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

console.log(completion.choices[0].message.content);

Model card
Architecture Overview:
• Reasoning model with native multimodal perception spanning images, documents, audio, and audio-video input
• 1M-token context window for whole-project codebase understanding
• Full architecture details arrive with the launch technical materials

Training Methodology:
• Trained specifically for agentic coding workflows on real developer work: multi-file refactors, debugging sessions, and long-running tasks
• Trained to browse the web, including multimodal browsing, and to work within an agentic harness: spawning and coordinating subagents, and using computer-use capabilities unified across desktop, mobile, and browser
• Trained on long-horizon, multi-site tasks, generating separate commentary that states intent before acting

Performance Characteristics:
• Benchmarks and performance data arrive with the launch announcement
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Prompting
Together AI API Access:
• Access Muse Spark Open via Together AI APIs using the endpoint meta-models/Muse-Spark-Open-816B-A42B
• Authenticate using your Together AI API key in request headers
• Supports text, image, audio, and video input across a 1M-token context window
• The model emits separate intent commentary on long-horizon tasks, so downstream systems can surface what it is doing and why
• Available on Together AI serverless and dedicated infrastructure
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Applications & use cases
Agentic Software Engineering:
• Hand off multi-file refactors, debugging sessions, and long-running engineering tasks end to end
• Hold entire repositories, dependencies, and legacy code in the 1M-token window without splitting work into pieces
• Chain reliable tool calls across long trajectories with self-correction built in

Computer Use & Web Automation:
• Automate workflows across desktop, mobile, and browser with unified computer-use capabilities
• Run long-horizon, multi-site tasks with the model browsing the web, including multimodal browsing
• Coordinate subagents for decomposable work under a single orchestrating session

Multimodal Document & Media Understanding:
• Reason over documents, images, audio, and video alongside code and text in one request
• Ground agent decisions in what the model sees, hears, and reads
• Surface transparent intent commentary for auditable automation
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