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What is Qwen 4?

Qwen 4 is Alibaba’s forthcoming next-generation foundation model and the planned successor to the company’s Qwen3.x model family. Alibaba announced Qwen 4 at the 2026 Apsara Conference on September 22 and confirmed that the model is currently in training. The company has not yet disclosed Qwen 4’s architecture, parameter count, context length, training-compute requirements, benchmark scores, pricing, licensing terms, or release schedule. Qwen 4 is being developed as Alibaba expands its broader AI stack across foundation models, multimodal systems, AI infrastructure, and agent-oriented cloud services. A major research direction surrounding Alibaba’s next generation of models is recursive self-improvement based on real-world tasks and empirical feedback. The company has already experimented with this approach using Qwen3.8-Max, allowing the model to participate in automated pipeline design, data validation, experimentation, error diagnosis, and post-training optimization. Alibaba reported that Qwen3.8-Max completed 33 iterative cycles during one such experiment and increased its Artificial Analysis score from 40 to 45. In a separate chip-design experiment, a Qwen model performed more than 10,000 EDA tool calls during over 60 hours of automated improvement work, illustrating Alibaba’s interest in long-horizon agentic tasks. These demonstrations describe the research program surrounding future Qwen development rather than confirmed features of Qwen 4 itself. Alibaba has also announced a longer-term roadmap in which Qwen 4.5 and Qwen 5 models are projected to reach between 5 trillion and 10 trillion parameters. Qwen 4 therefore remains a pre-release model, with detailed capabilities and access information expected to become clearer when Alibaba publishes its formal launch materials.

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.

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Integrations Supported

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Hugging Face
Model Context Protocol (MCP)
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen Code
Qwen Studio
QwenCloud
QwenWork

Integrations Supported

Nemotron 3

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online 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

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

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