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ease
features
design
support

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Description

Ling 3.0 Tiny is a reasoning model featuring open weights, comprising 7.9 billion total parameters and 1.3 billion active parameters, alongside a substantial context window of 262,000 tokens. Leveraging a mixture-of-experts architecture, it pushes the boundaries of the open-weights Pareto frontier in terms of intelligence relative to active parameters, while being compact enough for local deployment in various environments. Scoring 25 on the Artificial Analysis Intelligence Index, it stands on par with gpt-oss-120b, which scores 24, despite utilizing 15 times fewer total parameters and 4 times fewer active parameters. This impressive parameter efficiency does come with a trade-off, as it requires a significant 213 million output tokens to complete the Intelligence Index evaluation. In addition, Ling 3.0 Tiny exhibits noteworthy advancements in reducing hallucination tendencies compared to Ling-mini-2.0; it enhances its AA-Omniscience score by 59 points while keeping accuracy levels consistent. Notably, rather than making random guesses in uncertain situations, the model chose to attempt only 37% of the questions during evaluation, leading to a markedly reduced hallucination rate of 30%, a significant improvement over the previous generation's 96%. This strategic approach not only demonstrates the model's improved reasoning capabilities but also highlights its potential for more reliable real-world applications.

Description

Qwen3.5-35B-A3B is a member of the Qwen3.5 "Medium" model series, meticulously crafted as an effective multimodal foundation model that strikes a balance between robust reasoning capabilities and practical application needs. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a total of 35 billion parameters, yet activates only around 3 billion for each token, enabling it to achieve performance levels similar to much larger models while significantly cutting down on computational expenses. The model employs a hybrid attention mechanism that merges linear attention with traditional attention layers, which enhances its ability to handle extensive context and boosts scalability for intricate tasks. As an inherently vision-language model, it processes both textual and visual data, catering to a variety of applications, including multimodal reasoning, programming, and automated workflows. Furthermore, it is engineered to operate as a versatile "AI agent," proficient in planning, utilizing tools, and systematically solving problems, extending its functionality beyond mere conversational interactions. This capability positions it as a valuable asset across diverse domains, where advanced AI-driven solutions are increasingly required.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

OpenClaw
Claude Code
Hermes Agent
Hugging Face
Kilo Code
ModelScope
Ollama
OpenRouter
Qwen
Qwen Studio
ZenMux

Integrations

OpenClaw
Claude Code
Hermes Agent
Hugging Face
Kilo Code
ModelScope
Ollama
OpenRouter
Qwen
Qwen Studio
ZenMux

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Open source
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Ant Group

Founded

2014

Country

China

Website

ant-ling.com

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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