Open-Source AI

The world of large language models (LLMs) is evolving at breakneck speed. With each new release, the bar for performance, efficiency, and accessibility is raised. Enter Deep Seek v3.1—the latest breakthrough in open-source AI that’s making waves across the data science and AI communities.

Whether you’re a developer, researcher, or enterprise leader, understanding Deep Seek v3.1 is crucial for staying ahead in the rapidly changing landscape of artificial intelligence. In this guide, we’ll break down what makes Deep Seek v3.1 unique, how it compares to other LLMs, and how you can leverage its capabilities for your projects.

Uncover how brain-inspired architectures are pushing LLMs toward deeper, multi-step reasoning.

What is Deep Seek v3.1?

Deep Seek v3.1 is an advanced, open-source large language model developed by DeepSeek AI. Building on the success of previous versions, v3.1 introduces significant improvements in reasoning, context handling, multilingual support, and agentic AI capabilities.

Key Features at a Glance

• Hybrid Inference Modes:

  Supports both “Think” (reasoning) and “Non-Think” (fast response) modes for flexible deployment.

• Expanded Context Window:

  Processes up to 128K tokens (with enterprise versions supporting up to 1 million tokens), enabling analysis of entire codebases, research papers, or lengthy legal documents.

• Enhanced Reasoning:

  Up to 43% improvement in multi-step reasoning over previous models.

• Superior Multilingual Support:

  Over 100 languages, including low-resource and Asian languages.

• Reduced Hallucinations:

  38% fewer hallucinations compared to earlier versions.

• Open-Source Weights:

  Available for research and commercial use via Hugging Face.

• Agentic AI Skills:

  Improved tool use, multi-step agent tasks, and API integration for building autonomous AI agents.

Catch up on the evolution of LLMs and their applications in our comprehensive LLM guide.

Deep Dive: Technical Architecture of Deep Seek v3.1

Model Structure

• Parameters:

  671B total, 37B activated per token (Mixture-of-Experts architecture)

• Training Data:

  840B tokens, with extended long-context training phases

• Tokenizer:

  Updated for efficiency and multilingual support

• Context Window:

  128K tokens (with enterprise options up to 1M tokens)

• Hybrid Modes:

  Switch between “Think” (deep reasoning) and “Non-Think” (fast inference) via API or UI toggle

Hybrid Inference: Think vs. Non-Think

• Think Mode:

  Activates advanced reasoning, multi-step planning, and agentic workflows—ideal for complex tasks like code generation, research, and scientific analysis.

• Non-Think Mode:

  Prioritizes speed for straightforward Q&A, chatbots, and real-time applications.

Agentic AI & Tool Use

Deep Seek v3.1 is designed for the agent era, supporting:

• Strict Function Calling:

  For safe, reliable API integration

• Tool Use:

  Enhanced post-training for multi-step agent tasks

• Code & Search Agents:

  Outperforms previous models on SWE/Terminal-Bench and complex search tasks

Explore how agentic AI is transforming workflows in our Agentic AI Bootcamp.

Benchmarks & Performance: How Does Deep Seek v3.1 Stack Up?

Benchmark Results

DeepSeek-V3.1 demonstrates consistently strong benchmark performance across a wide range of evaluation tasks, outperforming both DeepSeek-R1-0528 and DeepSeek-V3-0324 in nearly every category. On browsing and reasoning tasks such as Browsecomp (30.0 vs. 8.9) and xbench-DeepSearch (71.2 vs. 55.0), V3.1 shows a clear lead, while also maintaining robust results in multi-step reasoning and information retrieval benchmarks like Frames (83.7) and SimpleQA (93.4). In more technically demanding evaluations such as SWE-bench Verified (66.0) and SWE-bench Multilingual (54.5), V3.1 delivers significantly higher accuracy than its counterparts, reflecting its capability for complex software reasoning. Terminal-Bench results further reinforce this edge, with V3.1 (31.3) scoring well above both V3-0324 and R1-0528. Interestingly, while R1-0528 tends to generate longer outputs, as seen in AIME 2025, GPQA Diamond, and LiveCodeBench, V3.1-Think achieves higher efficiency with competitive coverage, producing concise yet effective responses. Overall, DeepSeek-V3.1 stands out as the most balanced and capable model, excelling in both natural language reasoning and code-intensive benchmarks.

Real-World Performance

• Code Generation: Outperforms many closed-source models in code benchmarks and agentic tasks.
• Multilingual Tasks: Near-native proficiency in 100+ languages.
• Long-Context Reasoning: Handles entire codebases, research papers, and legal documents without losing context.

Learn more about LLM benchmarks and evaluation in our LLM Benchmarks Guide.

What’s New in Deep Seek v3.1 vs. Previous Versions?

Use Cases: Where Deep Seek v3.1 Shines

1. Software Development

• Advanced Code Generation: Write, debug, and refactor code in multiple languages.
• Agentic Coding Assistants: Build autonomous agents for code review, documentation, and testing.

2. Scientific Research

• Long-Context Analysis: Summarize and interpret entire research papers or datasets.
• Multimodal Reasoning: Integrate text, code, and image understanding for complex scientific workflows.

3. Business Intelligence

• Automated Reporting: Generate insights from large, multilingual datasets.
• Data Analysis: Perform complex queries and generate actionable business recommendations.

4. Education & Tutoring

• Personalized Learning: Multilingual tutoring with step-by-step explanations.
• Content Generation: Create high-quality, culturally sensitive educational materials.

5. Enterprise AI

• API Integration: Seamlessly connect Deep Seek v3.1 to internal tools and workflows.
• Agentic Automation: Deploy AI agents for customer support, knowledge management, and more.

See how DeepSeek is making high-powered LLMs accessible on budget hardware in our in-depth analysis.

Open-Source Commitment & Community Impact

Deep Seek v3.1 is not just a technical marvel—it’s a statement for open, accessible AI. By releasing both the full and smaller (7B parameter) versions as open source, DeepSeek AI empowers researchers, startups, and enterprises to innovate without the constraints of closed ecosystems.

• Download & Deploy: Hugging Face Model Card
• Community Integrations: Supported by major platforms and frameworks
• Collaborative Development: Contributions and feedback welcomed via GitHub and community forums

Explore the rise of open-source LLMs and their enterprise benefits in our open-source LLMs guide.

Pricing & API Access

• API Pricing:

  Competitive, with discounts for off-peak usage

• API Modes:

  Switch between Think/Non-Think for cost and performance optimization

• Enterprise Support:

  Custom deployments and support available

Getting Started with Deep Seek v3.1

1. Try Online:

   Use DeepSeek’s web interface for instant access (DeepSeek Chat)

2. Download the Model:

   Deploy locally or on your preferred cloud (Hugging Face)

3. Integrate via API:

   Connect to your applications using the documented API endpoints

4. Join the Community:

   Contribute, ask questions, and share use cases on GitHub and forums

Ready to build custom LLM applications? Check out our LLM Bootcamp.

Challenges & Considerations

• Data Privacy:

  As with any LLM, ensure sensitive data is handled securely, especially when using cloud APIs.

• Bias & Hallucinations:

  While Deep Seek v3.1 reduces hallucinations, always validate outputs for critical applications.

• Hardware Requirements:

  Running the full model locally requires significant compute resources; consider using smaller versions or cloud APIs for lighter workloads.

Learn about LLM evaluation, risks, and best practices in our LLM evaluation guide.

Frequently Asked Questions (FAQ)

Q1: How does Deep Seek v3.1 compare to GPT-4 or Llama 3?

A: Deep Seek v3.1 matches or exceeds many closed-source models in reasoning, context handling, and multilingual support, while remaining fully open-source and highly customizable.

Q2: Can I fine-tune Deep Seek v3.1 on my own data?

A: Yes! The open-source weights and documentation make it easy to fine-tune for domain-specific tasks.

Q3: What are the hardware requirements for running Deep Seek v3.1 locally?

A: The full model requires high-end GPUs (A100 or similar), but smaller versions are available for less resource-intensive deployments.

Q4: Is Deep Seek v3.1 suitable for enterprise applications?

A: Absolutely. With robust API support, agentic AI capabilities, and strong benchmarks, it’s ideal for enterprise-scale AI solutions.

Conclusion: The Future of Open-Source LLMs Starts Here

Deep Seek v3.1 is more than just another large language model—it’s a leap forward in open, accessible, and agentic AI. With its hybrid inference modes, massive context window, advanced reasoning, and multilingual prowess, it’s poised to power the next generation of AI applications across industries.

Whether you’re building autonomous agents, analyzing massive datasets, or creating multilingual content, Deep Seek v3.1 offers the flexibility, performance, and openness you need.

Ready to get started?

• Download Deep Seek v3.1
• Try the online demo
• Join Data Science Dojo’s LLM Bootcamp to master LLMs and agentic AI

The artificial intelligence community has a new champion in Falcon 180B, an open-source large language model (LLM) boasting a staggering 180 billion parameters, trained on a colossal dataset. This powerhouse newcomer has outperformed previous open-source LLMs on various fronts.

Falcon AI, particularly Falcon LLM 40B, represents a significant achievement by the UAE’s Technology Innovation Institute (TII). The “40B” designation indicates that this Large Language Model boasts an impressive 40 billion parameters.

Notably, TII has also developed a 7 billion parameter model, trained on a staggering 1500 billion tokens. In contrast, the Falcon LLM 40B model is trained on a dataset containing 1 trillion tokens from RefinedWeb. What sets this LLM apart is its transparency and open-source nature.

Falcon operates as an autoregressive decoder-only model and underwent extensive training on the AWS Cloud, spanning two months and employing 384 GPUs. The pretraining data predominantly comprises publicly available data, with some contributions from research papers and social media conversations.

Significance of Falcon AI

The performance of Large Language Models is intrinsically linked to the data they are trained on, making data quality crucial. Falcon’s training data was meticulously crafted, featuring extracts from high-quality websites, sourced from the RefinedWeb Dataset. This data underwent rigorous filtering and de-duplication processes, supplemented by readily accessible data sources.

Falcon’s architecture is optimized for inference, enabling it to outshine state-of-the-art models such as those from Google, Anthropic, Deepmind, and LLaMa, as evidenced by its ranking on the OpenLLM Leaderboard.

Beyond its impressive capabilities, Falcon AI distinguishes itself by being open-source, allowing for unrestricted commercial use. Users have the flexibility to fine-tune Falcon with their data, creating bespoke applications harnessing the power of this Large Language Model. Falcon also offers Instruct versions, including Falcon-7B-Instruct and Falcon-40B-Instruct, pre-trained on conversational data. These versions facilitate the development of chat applications with ease.

Hugging Face Hub Release

Announced through a blog post by the Hugging Face AI community, Falcon 180B is now available on Hugging Face Hub.

This latest-model architecture builds upon the earlier Falcon series of open-source LLMs, incorporating innovations like multiquery attention to scale up to its massive 180 billion parameters, trained on a mind-boggling 3.5 trillion tokens.

Unprecedented Training Effort

Falcon 180B represents a remarkable achievement in the world of open-source models, featuring the longest single-epoch pretraining to date. This milestone was reached using 4,096 GPUs working simultaneously for approximately 7 million GPU hours, with Amazon SageMaker facilitating the training and refinement process.

Surpassing LLaMA 2 & Commercial Models

To put Falcon 180B’s size in perspective, its parameters are 2.5 times larger than Meta’s LLaMA 2 model, previously considered one of the most capable open-source LLMs. Falcon 180B not only surpasses LLaMA 2 but also outperforms other models in terms of scale and benchmark performance across a spectrum of natural language processing (NLP) tasks.

It achieves a remarkable 68.74 points on the open-access model leaderboard and comes close to matching commercial models like Google’s PaLM-2, particularly on evaluations like the HellaSwag benchmark.

Falcon AI: A Strong Benchmark Performance

Falcon 180B consistently matches or surpasses PaLM-2 Medium on widely used benchmarks, including HellaSwag, LAMBADA, WebQuestions, Winogrande, and more. Its performance is especially noteworthy as an open-source model, competing admirably with solutions developed by industry giants.

Comparison with ChatGPT

When comparing Falcon 180B directly with ChatGPT, the distinctions become clear. The free version of ChatGPT is powered by GPT-3.5, and while it handles everyday queries well, Falcon 180B often delivers more precise and contextually rich responses. This is because Falcon 180B is engineered to offer enhanced natural language understanding, making it a step up from the free service.

On the other hand, ChatGPT Plus runs on GPT-4—a model known for its sophisticated reasoning and nuanced conversational abilities. In many evaluation benchmarks, Falcon 180B typically falls between GPT-3.5 and GPT-4, meaning it outperforms the free version but doesn’t quite match the advanced capabilities of the paid service.

This positioning makes Falcon 180B an exciting alternative for those seeking improved performance over GPT-3.5 without the premium commitment required for GPT-4, adding valuable diversity to the AI landscape.

Falcon AI with LangChain

LangChain is a Python library designed to facilitate the creation of applications utilizing Large Language Models (LLMs). It offers a specialized pipeline known as HuggingFacePipeline, tailored for models hosted on HuggingFace. This means that integrating Falcon with LangChain is not only feasible but also practical.

Installing LangChain package

Begin by installing the LangChain package using the following command:

This command will fetch and install the latest LangChain package, making it accessible for your use.

Creating a Pipeline for Falcon Model

Next, let’s create a pipeline for the Falcon model. You can do this by importing the required components and configuring the model parameters:

Here, we’ve utilized the HuggingFacePipeline object, specifying the desired pipeline and model parameters. The ‘temperature’ parameter is set to 0, reducing the model’s inclination to generate imaginative or off-topic responses. The resulting object, named ‘llm,’ stores our Large Language Model configuration.

You might also like: 6 best ChatGPT plugins for data science

PromptTemplate and LLMChain

LangChain offers tools like PromptTemplate and LLMChain to enhance the responses generated by the Large Language Model. Let’s integrate these components into our code:

In this section, we define a template for the PromptTemplate, outlining how our LLM should respond, emphasizing humor in this case. The template includes a question placeholder labeled {query}. This template is then passed to the PromptTemplate method and stored in the ‘prompt’ variable.

To finalize our setup, we combine the Large Language Model and the Prompt using the LLMChain method, creating an integrated model configured to generate humorous responses.

Putting It Into Action

Now that our model is configured, we can use it to provide humorous answers to user questions. Here’s an example code snippet:

In this example, we presented the query “How to reach the moon?” to the model, which generated a humorous response. The Falcon-7B-Instruct model followed the prompt’s instructions and produced an appropriate and amusing answer to the query.

This demonstrates just one of the many possibilities that this new open-source model, Falcon AI, can offer.

A Promising Future

Falcon 180B’s release marks a significant leap forward in the advancement of large language models. Beyond its immense parameter count, it showcases advanced natural language capabilities from the outset.

With its availability on Hugging Face, the model is poised to receive further enhancements and contributions from the community, promising a bright future for open-source AI.