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What is K2 Think?

K2 Think is an innovative open-source advanced reasoning model that has emerged from a collaborative effort between the Institute of Foundation Models at MBZUAI and G42. Despite having a relatively modest size of 32 billion parameters, K2 Think delivers performance that competes with top-tier models that possess much larger parameter counts. Its primary strength is in mathematical reasoning, where it has achieved excellent rankings on distinguished benchmarks, including AIME ’24/’25, HMMT ’25, and OMNI-Math-HARD. This model is part of a broader initiative aimed at developing open models in the UAE, which also encompasses Jais (for Arabic), NANDA (for Hindi), and SHERKALA (for Kazakh). It builds on the foundational work laid by the K2-65B, a fully reproducible open-source foundation model that was introduced in 2024. K2 Think is designed to be open, efficient, and versatile, featuring a web app interface that encourages user interaction and exploration. Its cutting-edge approach to parameter positioning signifies a notable leap forward in creating compact architectures for high-level AI reasoning. Furthermore, its development underscores a commitment to improving access to advanced AI technologies across multiple languages and sectors, ultimately fostering greater inclusivity in the field.

What is DeepScaleR?

DeepScaleR is an advanced language model featuring 1.5 billion parameters, developed from DeepSeek-R1-Distilled-Qwen-1.5B through a unique blend of distributed reinforcement learning and a novel technique that gradually increases its context window from 8,000 to 24,000 tokens throughout training. The model was constructed using around 40,000 carefully curated mathematical problems taken from prestigious competition datasets, such as AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. With an impressive accuracy rate of 43.1% on the AIME 2024 exam, DeepScaleR exhibits a remarkable improvement of approximately 14.3 percentage points over its base version, surpassing even the significantly larger proprietary O1-Preview model. Furthermore, its outstanding performance on various mathematical benchmarks, including MATH-500, AMC 2023, Minerva Math, and OlympiadBench, illustrates that smaller, finely-tuned models enhanced by reinforcement learning can compete with or exceed the performance of larger counterparts in complex reasoning challenges. This breakthrough highlights the promising potential of streamlined modeling techniques in advancing mathematical problem-solving capabilities, encouraging further exploration in the field. Moreover, it opens doors for developing more efficient models that can tackle increasingly challenging problems with great efficacy.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

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

Institute of Foundation Models

Company Location

United States

Company Website

www.k2think.ai/k2think

Company Facts

Organization Name

Agentica Project

Date Founded

2025

Company Location

United States

Company Website

agentica-project.com

Categories and Features

Categories and Features

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