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Description

DeepSeek-V4-Pro is an advanced Mixture-of-Experts language model built for high-performance reasoning, coding, and large-scale AI applications. With 1.6 trillion total parameters and 49 billion activated parameters, it delivers strong capabilities while maintaining computational efficiency. The model supports a massive context window of up to one million tokens, making it ideal for handling long documents and complex workflows. Its hybrid attention architecture improves efficiency by reducing computational overhead while maintaining accuracy. Trained on more than 32 trillion tokens, DeepSeek-V4-Pro demonstrates strong performance across knowledge, reasoning, and coding benchmarks. It includes advanced training techniques such as improved optimization and enhanced signal propagation for better stability. The model offers multiple reasoning modes, allowing users to choose between faster responses or deeper analytical thinking. It is designed to support agentic workflows and complex multi-step problem solving. As an open-source model, it provides flexibility for developers and organizations to customize and deploy at scale. Overall, DeepSeek-V4-Pro delivers a balance of performance, efficiency, and scalability for demanding AI applications.

Description

Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

Buda Yes 
C++ Yes 
Cheaper Inference Yes 
ClinePass Yes 
Dart Yes 
DeepSeek Yes 
DeepSeek Harness Yes 
Go Yes 
Java Yes 
JavaScript Yes 
Kubernetes Yes 
MoClaw Yes 
OfoxAI Yes 
OpenTag Yes 
PanGu Chat No 
PowerShell Yes 
Python Yes 
Reasonix Yes 
Rust Yes 
ZooClaw Yes 

Integrations

Buda No 
C++ No 
Cheaper Inference No 
ClinePass No 
Dart No 
DeepSeek No 
DeepSeek Harness No 
Go No 
Java No 
JavaScript No 
Kubernetes No 
MoClaw No 
OfoxAI No 
OpenTag No 
PanGu Chat Yes 
PowerShell No 
Python No 
Reasonix No 
Rust No 
ZooClaw No 

Pricing Details

$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 Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

DeepSeek

Founded

2023

Country

China

Website

deepseek.com

Vendor Details

Company Name

Huawei

Founded

1987

Country

China

Website

huawei.com

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