SubQ Snap

SubQ Snap

Subquadratic
+
+

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About

Muse Glimmer is a 30-billion-parameter open-weights model from Meta Superintelligence Labs, optimized for always-on local agent workflows. Small enough to run on a Mac or PC with a single consumer GPU, it is designed for local agents, function calling, coding, and LLM-as-a-judge evaluation without depending on cloud infrastructure or network access. The model combines long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following. It can complete end-to-end agentic tasks, sustain multi-step reasoning across extended workflows, recover from failed or unexpected tool calls, and accept interleaved text and images through a dedicated perception encoder for interpreting screenshots, charts, and documents. Muse Glimmer works with OpenClaw and other agentic orchestration patterns, supports controllable reasoning effort, and is trained on data from more than 100 languages.

About

SubQ Snap is a long-context decision model for enterprise and agentic workflows, built to make structured decisions quickly over large amounts of information. It handles tasks such as choosing which files are relevant, selecting the next tool, checking whether a result passes, deciding whether a workflow should continue, and determining when a frontier model is needed. Instead of generating open-ended text, Snap takes context, a question, and a finite set of allowed answers, then returns a probability distribution over those answers. It supports up to 384K tokens of mixed text and image context, allowing it to reason over codebases, contracts, interaction histories, logs, screenshots, charts, diagrams, and scanned pages in a single call. Powered by Subquadratic Sparse Attention, Snap reduces the cost of reading long inputs while a decision head maps contextual representations directly to admissible answers without autoregressive generation.

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

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

Developers building private, always-on AI agents that need strong reasoning, tool use, and multimodal capabilities on local hardware

Audience

Developers and enterprise AI teams interested in a tool to make fast, structured decisions across long documents, codebases, multimodal inputs, and agentic workflows

Support

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

Support

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

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version Not Supported
Free Trial Supported

Pricing

$0.05 per million input tokens
Free Version Not Supported
Free Trial Supported

Reviews/Ratings

Overall 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

  • Muse Glimmer is exciting because it brings serious AI capability closer to the device. An open-weight model that can run on a laptop or desktop is a big deal for developers, builders, and AI power users who want more control. I like that it is focused on agentic tasks, not just basic chat. If it can handle reasoning, coding help, workflow automation, and local experimentation well, it could be really useful for private projects and always-on agents. The open-weight angle is the biggest win. Being able to download, modify, and run the model locally makes Muse Glimmer feel much more flexible than a closed API-only model.

Cons

  • I would still want to test it hard before trusting it for serious work. Smaller local models can be impressive, but they still need to prove themselves on coding, tool use, long tasks, and messy real-world prompts. Running locally also means the experience depends on your hardware. Even if it works on consumer devices, performance, speed, and setup may vary a lot.

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
meta.ai/

Company Information

Subquadratic
Founded: 2024
United States
subq.ai/introducing-subq-snap

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Categories

Categories

AI Decision Models Supported

Integrations

ExecuTorch Supported
Hermes Agent Supported
Hugging Face Supported
LM Studio Supported
Meta AI Supported
Muse Code Supported
Ollama Supported
OpenClaw Supported
OpenCode Supported
Unsloth Supported

Integrations

ExecuTorch Not Supported
Hermes Agent Not Supported
Hugging Face Not Supported
LM Studio Not Supported
Meta AI Not Supported
Muse Code Not Supported
Ollama Not Supported
OpenClaw Not Supported
OpenCode Not Supported
Unsloth Not Supported
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