SubQ Snap

SubQ Snap

Subquadratic
+
+

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About

DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.

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 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 researchers, developers, and enterprises seeking a powerful open-source language model for large-scale reasoning, coding, and long-context AI applications

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 Not 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

$0.435 per 1M tokens (input)
$0.435 per 1 million input tokens (cache miss), $0.003625 per 1 million input tokens (cache hit), and $0.87 per 1 million output tokens
Free Version Supported
Free Trial Not 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:

Review this Software

Pros & Cons from Real Users

Pros

  • DeepSeek-V4-Pro is one of those models I keep coming back to because it handles serious work without feeling ridiculously expensive. For coding, repo analysis, long debugging threads, and agent-style workflows, the 1M-token context window is a huge advantage. I also like that it feels strong across both reasoning and implementation. I can use it to think through architecture, explain a messy bug, generate a fix, write tests, and then sanity-check the tradeoffs without constantly switching models. The Pro-Max reasoning mode is especially useful when I need it to slow down and really work through something. It is not the mode I would use for every quick answer, but for hard technical problems, it gives the model a lot more room to reason. The open-weight angle is a big plus too. As someone who uses it heavily, I like having more flexibility than a purely closed API model gives me.

Cons

  • It is still not something I would run on autopilot. For production code, I always review diffs, run tests, and check edge cases because even strong models can make confident mistakes. It can also be overkill for simple tasks. If I just need a quick explanation, small script, or lightweight edit, DeepSeek-V4-Flash may be the better fit. The size is another consideration. Open weights are great, but self-hosting a 1.6T-parameter MoE model is not casual infrastructure.

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

DeepSeek
Founded: 2023
China
deepseek.com

Company Information

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

Alternatives

Alternatives

Clef-flash

Clef-flash

Cloudflare
Clef

Clef

Cloudflare
Jev

Jev

TypeSafe AI
Liquid d1

Liquid d1

Liquid AI
DeepSeek-V4

DeepSeek-V4

DeepSeek
Strands Decider

Strands Decider

Strands Labs

Categories

AI Coding Models Supported
AI Models Supported

Categories

AI Decision Models Supported

Integrations

.NET Supported
Bash Supported
Buda Supported
C Supported
CSS Supported
Dart Supported
DeepSeek Harness Supported
Go Supported
Java Supported
Kubernetes Supported
Lua Supported
MoClaw Supported
Novita AI Supported
Objective-C Supported
Rust Supported
Solidity Supported
Together AI Supported
Vercel AI Gateway Supported
YAML Supported
ZooClaw Supported

Integrations

.NET Not Supported
Bash Not Supported
Buda Not Supported
C Not Supported
CSS Not Supported
Dart Not Supported
DeepSeek Harness Not Supported
Go Not Supported
Java Not Supported
Kubernetes Not Supported
Lua Not Supported
MoClaw Not Supported
Novita AI Not Supported
Objective-C Not Supported
Rust Not Supported
Solidity Not Supported
Together AI Not Supported
Vercel AI Gateway Not Supported
YAML Not Supported
ZooClaw Not Supported
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