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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

Muse Wonder is a frontier artificial intelligence model developed by Meta for personal assistance, professional workflows, software development, and autonomous agent tasks. The model combines Active Memory, which retrieves relevant information from previous conversations, with Active Focus, which supports sustained execution of complex, multistep assignments. Muse Wonder can assist with research, document analysis, financial and legal information, software engineering, and business process automation. Its agentic capabilities enable it to work across applications, use contextual information, adapt to obstacles, and request clarification or approval when necessary. The model supports coding activities ranging from small application development to complex software projects involving repeated testing and revision. Muse Wonder is available through the Meta Model API and Muse Code, with additional integration into Meta's Muse assistant planned for future releases.

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

Software developers, AI engineers, researchers, business professionals, knowledge workers, enterprise teams, and individuals seeking advanced AI assistance for complex reasoning, software development, personalized workflows, and autonomous task execution

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

$4 per 1M tokens (input)
Input: $4 per 1M tokens
Output: $20 per 1M tokens
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5

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.

Pros & Cons from Real Users

Pros

  • It feels much better at keeping track of what I am trying to accomplish across longer coding, research, and agent workflows instead of slowly losing the thread. Active Memory is especially useful. The model does a better job carrying important context forward, which makes extended projects feel less like repeatedly briefing a new assistant. I also like the new Active Focus approach. Wonder feels more deliberate about working toward the actual goal instead of getting distracted by side issues, which matters a lot when I give an agent something that takes many steps to finish. The coding ability is strong too. Meta is positioning Wonder against models like GPT-6 Astra and Claude Opus 5.5, and early reporting says it is especially competitive in software development and professional agentic work.

Cons

  • No real cons so far. I am still not sure how efficient pricing is.

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

Meta
Founded: 2004
United States
meta.ai/

Alternatives

Alternatives

DeepSeek-V4

DeepSeek-V4

DeepSeek

Categories

AI Coding Models Supported
AI Models Supported

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported
Multimodal Models Supported

Integrations

.NET Supported
C Supported
C# Supported
C++ Supported
CSS Supported
HTML Supported
Java Supported
JavaScript Supported
Kotlin Supported
Kubernetes Supported
Objective-C Supported
OpenClaw Supported
PowerShell Supported
Python Supported
Rust Supported
Scala Supported
Swift Supported
TypeScript Supported
XML Supported
YAML Supported

Integrations

.NET Supported
C Supported
C# Supported
C++ Supported
CSS Supported
HTML Supported
Java Supported
JavaScript Supported
Kotlin Supported
Kubernetes Supported
Objective-C Supported
OpenClaw Supported
PowerShell Supported
Python Supported
Rust Supported
Scala Supported
Swift Supported
TypeScript Supported
XML Supported
YAML Supported
Claim DeepSeek-V4-Pro and update features and information
Claim DeepSeek-V4-Pro and update features and information
Claim Muse Wonder and update features and information
Claim Muse Wonder and update features and information