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What is Qwen3?

Qwen3, the latest large language model from the Qwen family, introduces a new level of flexibility and power for developers and researchers. With models ranging from the high-performance Qwen3-235B-A22B to the smaller Qwen3-4B, Qwen3 is engineered to excel across a variety of tasks, including coding, math, and natural language processing. The unique hybrid thinking modes allow users to switch between deep reasoning for complex tasks and fast, efficient responses for simpler ones. Additionally, Qwen3 supports 119 languages, making it ideal for global applications. The model has been trained on an unprecedented 36 trillion tokens and leverages cutting-edge reinforcement learning techniques to continually improve its capabilities. Available on multiple platforms, including Hugging Face and ModelScope, Qwen3 is an essential tool for those seeking advanced AI-powered solutions for their projects.

What is Olmo 2?

OLMo 2 is a suite of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with straightforward access to training datasets, open-source code, reproducible training methods, and extensive evaluations. These models are trained on a remarkable dataset consisting of up to 5 trillion tokens and are competitive with leading open-weight models such as Llama 3.1, especially in English academic assessments. A significant emphasis of OLMo 2 lies in maintaining training stability, utilizing techniques to reduce loss spikes during prolonged training sessions, and implementing staged training interventions to address capability weaknesses in the later phases of pretraining. Furthermore, the models incorporate advanced post-training methodologies inspired by AI2's Tülu 3, resulting in the creation of OLMo 2-Instruct models. To support continuous enhancements during the development lifecycle, an actionable evaluation framework called the Open Language Modeling Evaluation System (OLMES) has been established, featuring 20 benchmarks that assess vital capabilities. This thorough methodology not only promotes transparency but also actively encourages improvements in the performance of language models, ensuring they remain at the forefront of AI advancements. Ultimately, OLMo 2 aims to empower the research community by providing resources that foster innovation and collaboration in language modeling.

Media

Media

Integrations Supported

AiAssistWorks
Baseten
Elixir
ExecuTorch
Go
Holo2
Java
Kaggle
LFM2
Mirai
OpenClaw
Qwen Studio
R
Ruby
Runpod
SQL
Scala
TypeScript
Visual Basic
kluster.ai

API Availability

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwenlm.github.io/blog/qwen3/

Company Facts

Organization Name

Ai2

Date Founded

2014

Company Location

United States

Company Website

allenai.org/blog/olmo2

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

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