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What is DeepSeek V3.1?

DeepSeek V3.1 emerges as a groundbreaking open-weight large language model, featuring an astounding 685-billion parameters and an extensive 128,000-token context window that enables it to process lengthy documents similar to 400-page novels in a single run. This model encompasses integrated capabilities for conversation, reasoning, and code generation within a unified hybrid framework that effectively blends these varied functionalities. Additionally, V3.1 supports multiple tensor formats, allowing developers to optimize performance across different hardware configurations. Initial benchmark tests indicate impressive outcomes, with a notable score of 71.6% on the Aider coding benchmark, placing it on par with or even outperforming competitors like Claude Opus 4, all while maintaining a significantly lower cost. Launched under an open-source license on Hugging Face with minimal promotion, DeepSeek V3.1 aims to transform the availability of advanced AI solutions, potentially challenging the traditional landscape dominated by proprietary models. The model's innovative features and affordability are likely to attract a diverse array of developers eager to implement state-of-the-art AI technologies in their applications, thus fostering a new wave of creativity and efficiency in the tech industry.

What is Beam?

Beam marks the launch of Reflection’s first open-weight model, distinguished by its sparse Mixture-of-Experts architecture, which boasts an impressive 501 billion parameters, with 23 billion actively engaged, specifically designed for tasks related to coding, reasoning, and agentic functions. This model's capabilities stem from rigorous pretraining and reinforcement learning, built upon a massive dataset of 23.8 trillion diverse, high-quality tokens obtained from various sources, including the internet, public domains, and proprietary licenses. By focusing on optimizing coding and agentic functionalities, Beam aims to deliver competitive performance in the open-weight space while prioritizing efficient inference computation. It is proficient in managing a diverse range of tasks, such as complex software development, terminal commands, STEM-related activities, web searches, tool application, and general knowledge queries. Utilizing reinforcement learning methods enhances its proficiency in multi-step reasoning, effective tool use, and adaptability to environmental cues. Furthermore, users can customize the model's output by adjusting a reasoning effort parameter, allowing for a tailored balance between efficiency and performance to meet their individual requirements. In essence, Beam is not only a technological advancement but also a versatile tool that empowers users to navigate complex tasks with precision.

Media

Media

Integrations Supported

Aider
EaseMate AI
Hugging Face
Intrascope
LLM Council
Microsoft Foundry Models
ModelArk
Nebius Token Factory
Okara
Ollama

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
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
Online Training

Company Facts

Organization Name

DeepSeek

Date Founded

2023

Company Location

China

Company Website

deepseek.ai/blog/deepseek-v31

Company Facts

Organization Name

Reflection

Company Location

United States

Company Website

reflection.ai/blog/introducing-beam

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

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