Models
DecentralGPT Network encompasses various GPT models, including both open-source and closed-source models.
Users can select different models to perform tasks as needed.
Model developers can also submit their models to the DecentralGPT Network.
All user data is encrypted and stored on a decentralized storage network, making it inaccessible to unauthorized parties.
We already support the world's most powerful LLM model-- DeepSeek、LIama、Qwen
Qwen3 235B
Chinese task priority - Alibaba's open-source large model supports long text and multimodality, making it suitable for enterprise-level complex scenario deployment.
- Development Team: Alibaba
- Launch Date: 2025.04
- Model Parameters: 235B
- Features: Large-scale open-source MoE model, supporting multimodality and high-performance inference, suitable for complex tasks and long-text processing.
DeepSeek V3
Logic Reasoning Enhanced Version - Excels in mathematics, coding, and chain-of-thought tasks, suitable for scenarios requiring rigorous reasoning.
- Development Team: DeepSeek
- Launch Date: 2024.12
- Model Parameters: 670B
- Features: Supports 128K long text understanding, strong code and mathematical capabilities, open source, suitable for complex reasoning and generation tasks.
Qwen3 235B Thinking
Long-text Expert – Supports 128K context, boasts strong code and mathematical capabilities, open-source and commercially available, with extremely high cost-performance.
- Development Team: Alibaba
- Launch Date: 2025.04
- Model Parameters: 235B
- Features: Enhanced Reasoning Qwen3, optimized for logical reasoning and chain-of-thought (CoT), suitable for complex decision-making and mathematical derivation.
DeepSeek R1
For complex decision-making and mathematical derivation. DeepSeek R1 - The Terminator of Complex Problems: Optimized for reasoning, suitable for high-difficulty tasks such as math competitions and algorithm research.
- Development Team: DeepSeek
- Launch Date: 2025.01
- Model Parameters: 670B
- Features: Reinforcement Learning Optimized Version, enabling efficient fine-tuning and suitable for practical application scenarios such as search and recommendation.
Llama-4-Scout-Instruct
An open-source, instruction-tuned LLM by Meta and Hugging Face, optimized for English with multilingual support, based on Llama
- Development Team: meta and Hugging Face
- Launch Date: 2025.04
- Model Parameters: 70B
- Features: Fully open-source and free, risk-free for private deployment. Perfect for developers and enterprises to build English-focused AI assistants — from auto-writing emails and reviewing contracts to private knowledge base QA, effortlessly handling 128K long-text contexts.