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What is Qwen3.8-2.4T-A95B?

Qwen3.8-2.4T-A95B emerges as the largest open model in the Qwen3.8 series, presenting advanced Qwen-Max-class capabilities in a format that is accessible to the public. Built on the robust foundation of Qwen3.5, this model offers marked improvements in performance across various domains, including coding, professional applications, research, and complex, extended agentic tasks, underscoring its ability to reliably execute intricate, multi-step workflows to completion. With its innovative mixture-of-experts architecture, it features a remarkable total of 2.4 trillion parameters, of which 95 billion are activated, utilizing 512 experts and allowing for simultaneous engagement of 10 routed experts alongside one shared expert. The model supports a native context length of 262,144 tokens, extendable to about 1.01 million tokens, thereby enabling considerable adaptability for diverse applications. Additionally, enhancements in agent execution, such as superior autonomous planning and improved responsiveness to environmental cues, enhance its overall efficiency. Its extensive compatibility with popular agent frameworks and development tools further aids in smooth integration into current systems, making it an appealing option for both developers and researchers. This versatility is particularly beneficial for those seeking to leverage advanced AI capabilities in their projects.

What is Nemotron 3 Nano?

The Nemotron 3 Nano distinguishes itself as the smallest model in NVIDIA's Nemotron 3 series, tailored specifically for agentic AI applications that necessitate strong reasoning and conversational capabilities while ensuring economical inference costs. This innovative hybrid Mamba-Transformer Mixture-of-Experts model is equipped with 3.2 billion active parameters and expands to 3.6 billion when accounting for embeddings, culminating in an impressive total of 31.6 billion parameters. NVIDIA claims that this model achieves superior accuracy compared to its predecessor, the Nemotron 2 Nano, while also operating with less than half of the parameters during each forward pass, thereby boosting efficiency without sacrificing performance. Additionally, it reportedly outperforms both GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507 across a range of commonly used benchmarks. With an input capacity of 8K and an output limit of 16K utilizing a single H200, the model realizes an inference throughput that is 3.3 times higher than that of Qwen3-30B-A3B and 2.2 times that of GPT-OSS-20B. Furthermore, the Nemotron 3 Nano can manage context lengths of up to 1 million tokens, reinforcing its dominance over GPT-OSS-20B and Qwen3-30B-A3B-Instruct-2507. This extraordinary amalgamation of capabilities not only enhances its precision and efficiency but also positions the Nemotron 3 Nano as a premier option for cutting-edge AI endeavors that require top-tier performance. As the demand for advanced AI solutions grows, the relevance of such models will likely continue to expand.

Media

Media

Integrations Supported

Alibaba Cloud
Alibaba Cloud Model Studio
Cline
ClinePass
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Nemotron 3
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

Integrations Supported

Alibaba Cloud
Alibaba Cloud Model Studio
Cline
ClinePass
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
ModelScope
Nemotron 3
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

nvidia.com

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