Smaug Flash

Smaug Flash

Abacus.AI
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About

Muse Spark Open is an open-weight, natively multimodal language model for developers and organizations building AI applications, coding systems, and agentic workflows. The released model has 816 billion total parameters, 42 billion active parameters, and a 1-million-token context window. Teams can download the model weights, run inference through supported engines such as vLLM, SGLang, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, or Unsloth, and customize the model using PyTorch. It supports long-context reasoning, software engineering, coding, tool use, and multimodal inputs. Deployment options include cloud inference through listed API providers or self-hosting on compatible GPU systems; Meta documents a desktop setup using two Apple M5 Ultra Mac Studio computers. The model is released under the Muse Model License with limited commercial terms.

About

Smaug Flash is a family of three open-weight models fine-tuned by Abacus.AI for production agentic workloads, with each model positioned at a different point on the capability–efficiency curve. The line is trained using human-curated real-world agentic traces combined with synthetic data grounded in difficult examples, producing gains in agentic coding, real-world tool use, automation, long-context reasoning, and instruction following. Smaug Flash, based on DeepSeek V4 Flash 0731, is the workhorse model for enterprise self-improving agents where speed, efficiency, and reliable agent performance need to coexist. It is specifically tuned to reduce the spins and confusion that can appear during long-context tool use while retaining the base model’s speed advantages. Smaug Mini, based on Qwen3.8 27B, targets multimodal use cases and smaller reasoning tasks in a more compact package, with stronger real-world agentic ability for one-off workflows.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

AI developers, machine learning engineers, software engineers, AI researchers, enterprise development teams, and organizations seeking customizable open-weight multimodal models for local inference, autonomous agents, complex coding, and long-context AI applications

Audience

Developers, AI researchers, and enterprises seeking models optimized for coding, tool use, automation, multimodal tasks, and long-running agentic workflows

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$0
Model weights are available at no charge under the Muse Model License, which has limited commercial-use terms. Hosted inference providers set their own usage prices.
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Pros & Cons from Real Users

Pros

  • Meta finally opened up a genuinely frontier-level Muse model. The 1M-token context window and 42B active parameters make it strong enough for large repos, long agent sessions, multimodal work, and complex coding without feeling like a stripped-down open model. The local deployment story is also a huge deal. Being able to run something this capable on two M5 Ultra Mac Studios is impressive, especially for teams that want more control over data, infrastructure, and customization. I also like how much of the stack is open around it. Muse Spark Open works with MLX, vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, and Unsloth, and Meta is making the weights available through Hugging Face, SageMaker, Google Cloud Model Garden, and Microsoft Foundry.

Cons

  • Even though it is technically desktop-capable, needing two M5 Ultra Mac Studios for the full local experience is still a serious setup, so most developers will probably use hosted inference instead.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Meta
Founded: 2004
United States
research.meta.ai/blog/muse-spark-open

Company Information

Abacus.AI
Founded: 2019
United States
abacus.ai/smaug

Alternatives

Alternatives

MiniMax M3

MiniMax M3

MiniMax
DeepSeek-V4

DeepSeek-V4

DeepSeek
Hy3

Hy3

Tencent
Recursive Flint

Recursive Flint

Recursive
Qwen3.6-Plus

Qwen3.6-Plus

Alibaba

Categories

Categories

AI Coding Models Supported

Integrations

Baseten Supported
Fireworks AI Supported
Groq Supported
Hugging Face Supported
Modal Supported
Nebius Supported
OpenRouter Supported
Together AI Supported
vLLM Supported

Integrations

Baseten Not Supported
Fireworks AI Not Supported
Groq Not Supported
Hugging Face Not Supported
Modal Not Supported
Nebius Not Supported
OpenRouter Not Supported
Together AI Not Supported
vLLM Not Supported
Claim Muse Spark Open and update features and information
Claim Muse Spark Open and update features and information
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