DeepSeek R1

DeepSeek R1

DeepSeek
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About

DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Researchers, developers, and organizations seeking advanced, open-source reasoning models for complex tasks like mathematics, programming, and AI-driven problem-solving

Audience

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

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

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
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5

Pros & Cons from Real Users

Pros

  • DeepSeek-R1 is a remarkable AI model that has significantly enhanced my data analysis tasks. Its open-source nature allows for extensive customization, making it adaptable to various project needs. The model's performance is on par with leading AI models, yet it operates at a fraction of the cost, which is a substantial advantage. Additionally, DeepSeek-R1's integration capabilities with web, app, and API platforms provide flexibility in deployment across different environments.

Cons

  • Haven't found any cons yet. This model is so capable.

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
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

DeepSeek
Founded: 2023
China
www.deepseek.com

Company Information

DeepSeek
Founded: 2023
China
deepseek.com

Alternatives

Alternatives

Claude Opus 5

Claude Opus 5

Anthropic
Claude Fable 5

Claude Fable 5

Anthropic
Claude Sonnet 4

Claude Sonnet 4

Anthropic
Claude Mythos 5

Claude Mythos 5

Anthropic
DeepSeek R2

DeepSeek R2

DeepSeek
DeepSeek-V4

DeepSeek-V4

DeepSeek

Categories

Categories

Integrations

C
C#
C++
CSS
DeepSeek
Go
HTML
Java
JavaScript
Kotlin
Kubernetes
Oxlo.ai
Python
R
Ruby
Rust
SQL
Scala
TypeScript
StepFun

Integrations

C
C#
C++
CSS
DeepSeek
Go
HTML
Java
JavaScript
Kotlin
Kubernetes
Oxlo.ai
Python
R
Ruby
Rust
SQL
Scala
TypeScript
StepFun
Claim DeepSeek R1 and update features and information
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