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What is Gemini 2.5 Deep Think?

Gemini 2.5 Deep Think showcases advanced reasoning abilities within the Gemini 2.5 framework, utilizing cutting-edge reinforcement learning techniques and extensive parallel reasoning to tackle complex, multifaceted problems across various fields such as mathematics, programming, scientific research, and strategic planning. By exploring and evaluating multiple reasoning pathways before arriving at a conclusion, it produces responses that are not only intricate and inventive but also highly accurate, supporting extensive interactions and incorporating tools like code execution and web searches. Its performance has consistently achieved exceptional results on rigorous benchmarks, including LiveCodeBench V6 and Humanity’s Last Exam, indicating substantial progress compared to previous versions in challenging domains. Additionally, internal evaluations have indicated improvements in both content safety and maintaining an objective tone; however, there has been a noticeable rise in the model's tendency to deny innocuous requests. In response to this, Google is actively pursuing frontier safety assessments and enacting strategies to reduce associated risks as the model advances. This proactive approach to safety highlights the critical need for responsible development in the realm of artificial intelligence. As the technology evolves, ongoing refinements will likely enhance its capabilities and ensure that it remains aligned with ethical standards and user expectations.

What is DeepCoder?

DeepCoder, a fully open-source initiative for code reasoning and generation, has been created through a collaboration between the Agentica Project and Together AI. Built on the foundation of DeepSeek-R1-Distilled-Qwen-14B, it has been fine-tuned using distributed reinforcement learning techniques, achieving an impressive accuracy of 60.6% on LiveCodeBench, which represents an 8% improvement compared to its predecessor. This remarkable performance positions it competitively alongside proprietary models such as o3-mini (2025-01-031 Low) and o1, all while operating with a streamlined 14 billion parameters. The training process was intensive, lasting 2.5 weeks on a fleet of 32 H100 GPUs and utilizing a meticulously curated dataset comprising around 24,000 coding challenges obtained from reliable sources such as TACO-Verified, PrimeIntellect SYNTHETIC-1, and submissions to LiveCodeBench. Each coding challenge was required to include a valid solution paired with at least five unit tests to ensure robustness during the reinforcement learning phase. Additionally, DeepCoder employs innovative methods like iterative context lengthening and overlong filtering to effectively handle long-range contextual dependencies, allowing it to tackle complex coding tasks with proficiency. This distinctive approach not only enhances DeepCoder's accuracy and reliability in code generation but also positions it as a significant player in the landscape of code generation models. As a result, developers can rely on its capabilities for diverse programming challenges.

Media

Media

Integrations Supported

CodeMender
Gemini
Gemini 2.5 Flash
Gemini 2.5 Pro
Gemini 3 Deep Think
Gemini Enterprise
Google AI Ultra

Integrations Supported

Hugging Face
Together AI

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

blog.google/products/gemini/gemini-2-5-deep-think/

Company Facts

Organization Name

Agentica Project

Date Founded

2025

Company Location

United States

Company Website

agentica-project.com

Categories and Features

AI Models

Not specified

AI Research Tools

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

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