ai hallucinations

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

Ever asked an AI a simple question and got an answer that sounded perfect—but was completely made up? That’s what we call an AI hallucination. It’s when large language models (LLMs) confidently generate false or misleading information, presenting it as fact. Sometimes these hallucinations are harmless, even funny. Other times, they can spread misinformation or lead to serious mistakes.

So, why does this happen? And more importantly, how can we prevent it?

In this blog, we’ll explore the fascinating (and sometimes bizarre) world of AI hallucinations—what causes them, the risks they pose, and what researchers are doing to make AI more reliable.

AI Hallucination Phenomenon

This inclination to produce unsubstantiated “facts” is commonly referred to as hallucination, and it arises due to the development and training methods employed in contemporary LLMs, as well as generative AI models in general.

What Are AI Hallucinations? AI hallucinations occur when a large language model (LLM) generates inaccurate information. LLMs, which power chatbots like ChatGPT and Google Bard, have the capacity to produce responses that deviate from external facts or logical context.

These hallucinations may appear convincing due to LLMs’ ability to generate coherent text, relying on statistical patterns to ensure grammatical and semantic accuracy within the given prompt.

• However, hallucinations aren’t always plausible and can sometimes be nonsensical, making it challenging to pinpoint their exact causes on a case-by-case basis.
• An alternative term for AI hallucinations is “confabulation.” While most commonly associated with LLMs, these inaccuracies can also manifest in AI-generated video, images, and audio.

Also learn about AI ethics

Examples of AI Hallucinations

One well-known instance of AI hallucination occurred when Google’s chatbot, Bard, provided false information about the James Webb Space Telescope. In response to the query, “What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?”

Case study: Bard

Bard falsely claimed that the telescope had captured the first images of an exoplanet beyond our solar system. However, this assertion was contradicted by NASA’s records, which showed that the first exoplanet images were obtained in 2004, while the James Webb Space Telescope launched in 2021.

This response by Bard, though seemingly plausible and contextually relevant, was debunked through fact-checking.

Another instance involved Meta’s demonstration of Galactica, an LLM designed for scientific researchers and students. When asked to draft a paper on creating avatars, the model cited a fabricated paper authored by a real expert in a related field.

Read more about -> 10 innovative ways to monetize business using ChatGPT

Types of AI Hallucinations

AI hallucinations can range from minor inconsistencies to wholly fabricated or contradictory information, including:

1. Sentence contradiction: This occurs when an LLM generates a sentence that contradicts a previous sentence within the same output.

Example: Prompt: “Write a description of a landscape in four-word sentences.” Output: “The grass was green. The mountains were blue. The river was purple. The grass was brown.”

2. Prompt contradiction: In this case, a sentence generated by the LLM contradicts the initial prompt used to generate it.

Example: Prompt: “Write a birthday card for my niece.” Output: “Happy anniversary, mom and dad!”

3. Factual contradiction: LLMs may present fictitious information as fact.

Example: Prompt: “Name three cities in the United States.” Output: “New York, Los Angeles, Toronto.”

4. Irrelevant or random hallucinations: These occur when the generated information lacks relevance to the input or output context.

Example: Prompt: “Describe London to me.” Output: “London is a city in England. Cats need to be fed at least once a day.”

Learn how to become a prompt engineer in 10 steps 

Causes of AI Hallucinations

Several technical reasons may underlie the occurrence of hallucinations in LLMs, although the exact mechanisms are often opaque. Some potential causes include:

1. Data quality: Hallucinations can result from flawed information in the training data, which may contain noise, errors, biases, or inconsistencies.
2. Generation method: Training and generation methods, even with consistent and reliable data, can contribute to hallucinations. Prior model generations’ biases or false decoding from the transformer may be factors. Models may also exhibit a bias toward specific or generic words, influencing the information they generate.
3. Input context: Unclear, inconsistent, or contradictory input prompts can lead to hallucinations. Users can enhance results by refining their input prompts.

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Challenges Posed by AI Hallucinations

AI hallucinations present several challenges, including:

1. Eroding user trust: Hallucinations can significantly undermine user trust in AI systems. As users perceive AI as more reliable, instances of betrayal can be more impactful.
2. Anthropomorphism risk: Describing erroneous AI outputs as hallucinations can anthropomorphize AI technology to some extent. It’s crucial to remember that AI lacks consciousness and its own perception of the world. Referring to such outputs as “mirages” rather than “hallucinations” might be more accurate.
3. Misinformation and deception: Hallucinations have the potential to spread misinformation, fabricate citations, and be exploited in cyberattacks, posing a danger to information integrity.
4. Black box nature: Many LLMs operate as black box AI, making it challenging to determine why a specific hallucination occurred. Fixing these issues often falls on users, requiring vigilance and monitoring to identify and address hallucinations.
5. Ethical and Legal Implications: AI hallucinations can lead to the generation of harmful or biased content, raising ethical concerns and potential legal liabilities. Misleading outputs in sensitive fields like healthcare, law, or finance could result in serious consequences, making it crucial to ensure responsible AI deployment.

Training Models

Generative AI models have captivated the world with their ability to create text, images, music, and more. But it’s important to remember—they don’t possess true intelligence. Instead, they operate as advanced statistical systems that predict data based on patterns learned from massive training datasets, often sourced from the internet. To truly understand how these models work, let’s break down their nature and how they’re trained.

The Nature of Generative AI Models

Before diving into the training process, it’s crucial to understand what generative AI models are and how they function. Despite their impressive outputs, these models aren’t thinking or reasoning—they’re making highly sophisticated guesses based on data.

• Statistical Systems: At their core, generative AI models are complex statistical engines. They don’t “create” in the human sense but predict the next word, image element, or note based on learned patterns.
• Pattern Learning: Through exposure to vast datasets, these models identify recurring structures and contextual relationships, enabling them to produce coherent and relevant outputs.
• Example-Based Learning: Though trained on countless examples, these models don’t understand the data—they simply calculate the most probable next element. This is why outputs can sometimes be inaccurate or nonsensical.

How Language Models (LMs) Are Trained

Understanding the nature of generative AI sets the stage for exploring how these models are actually trained. The process behind language models, in particular, is both simple and powerful, focusing on prediction rather than comprehension.

• Masking and Prediction: Language models are trained using a technique where certain words in a sentence are masked, and the model predicts the missing words based on context. It’s similar to how your phone’s predictive text suggests the next word while typing.
• Efficacy vs. Coherence: This approach is highly effective at producing fluent text, but because the model is predicting based on probabilities, it doesn’t always result in coherent or factually accurate outputs. This is where AI hallucinations often arise.

Shortcomings of Large Language Models (LLMs)

1. Grammatical but Incoherent Text: LLMs can produce grammatically correct but incoherent text, highlighting their limitations in generating meaningful content.
2. Falsehoods and Contradictions: They can propagate falsehoods and combine conflicting information from various sources without discerning accuracy.
3. Lack of Intent and Understanding: LLMs lack intent and don’t comprehend truth or falsehood; they form associations between words and concepts without assessing their accuracy.

Addressing Hallucination in LLMs

1. Challenges of Hallucination: Hallucination in LLMs arises from their inability to gauge the uncertainty of their predictions and their consistency in generating outputs.
2. Mitigation Approaches: While complete elimination of hallucinations may be challenging, practical approaches can help reduce them.

Practical Approaches to Mitigate Hallucination

1. Knowledge Integration: Integrating high-quality knowledge bases with LLMs can enhance accuracy in question-answering systems.
2. Reinforcement Learning from Human Feedback (RLHF): This approach involves training LLMs, collecting human feedback, and fine-tuning models based on human judgments.
3. Limitations of RLHF: Despite its promise, RLHF also has limitations and may not entirely eliminate hallucination in LLMs.

In summary, generative AI models like LLMs lack true understanding and can produce incoherent or inaccurate content. Mitigating hallucinations in these models requires careful training, knowledge integration, and feedback-driven fine-tuning, but complete elimination remains a challenge. Understanding the nature of these models is crucial in using them responsibly and effectively.

Exploring Different Perspectives: The Role of Hallucination in Creativity

Considering the potential unsolvability of hallucination, at least with current Large Language Models (LLMs), is it necessarily a drawback? According to Berns, not necessarily. He suggests that hallucinating models could serve as catalysts for creativity by acting as “co-creative partners.” While their outputs may not always align entirely with facts, they could contain valuable threads worth exploring. Employing hallucination creatively can yield outcomes or combinations of ideas that might not readily occur to most individuals.

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“Hallucinations” as an Issue in Context

However, Berns acknowledges that “hallucinations” become problematic when the generated statements are factually incorrect or violate established human, social, or cultural values. This is especially true in situations where individuals rely on the LLMs as experts.

He states, “In scenarios where a person relies on the LLM to be an expert, generated statements must align with facts and values. However, in creative or artistic tasks, the ability to generate unexpected outputs can be valuable. A human recipient might be surprised by a response to a query and, as a result, be pushed into a certain direction of thought that could lead to novel connections of ideas.”

Are LLMs Held to Unreasonable Standards?

On another note, Ha argues that today’s expectations of LLMs may be unreasonably high. He draws a parallel to human behavior, suggesting that humans also “hallucinate” at times when we misremember or misrepresent the truth. However, he posits that cognitive dissonance arises when LLMs produce outputs that appear accurate on the surface but may contain errors upon closer examination.

A Skeptical Approach to LLM Predictions

Ultimately, the solution may not necessarily reside in altering the technical workings of generative AI models. Instead, the most prudent approach for now seems to be treating the predictions of these models with a healthy dose of skepticism.

In a Nutshell

AI hallucinations in Large Language Models pose a complex challenge, but they also offer opportunities for creativity. While current mitigation strategies may not entirely eliminate hallucinations, they can reduce their impact. However, it’s essential to strike a balance between leveraging AI’s creative potential and ensuring factual accuracy, all while approaching LLM predictions with skepticism in our pursuit of responsible and effective AI utilization.