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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.
Integrations Supported
AiAssistWorks
Baseten
Elixir
ExecuTorch
Go
Holo2
Java
Kaggle
LFM2
Mirai
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