DeepSeek R1 vs Manus AI: A Comprehensive Comparison

DeepSeek R1 and Manus AI are two AI systems that have gained attention in the AI community. DeepSeek R1 is a first-generation large language model (LLM), released by the startup DeepSeek, focused on advanced reasoning. It has advanced reasoning capabilities and high precision on complex tasks, often matching top proprietary models at a fraction of their training cost. This cost-efficiency and openness have made DeepSeek R1 a landmark in the AI landscape. The “DeepSeek moment” label highlights the ability of open models to compete with closed models at lower costs.

Manus AI is an autonomous AI agent created by Chinese startup Monica. It is the world’s first fully autonomous general AI agent. Unlike traditional chatbots, Manus can plan and execute multi-step tasks. This enables Manus to perform actions across different tools and websites based on a user’s request. In early demonstrations, Manus managed tasks like travel planning and financial analysis without human help. Its launch in March 2025 generated excitement in the tech community, with some comparing it to DeepSeek’s debut. Many view it as a pivotal moment in China’s tech industry because of its rapid viral success and remarkable autonomy.

This comparison is important because DeepSeek R1 and Manus AI represent two different directions in AI development. DeepSeek R1 focuses on model architecture and training methods for reasoning, pushing the limits of a single model’s intelligence. Manus AI emphasizes agentic AI, combining models with tools and autonomy to achieve tasks that static models cannot. By comparing them, we can understand AI development from model design to real-world impact. In the following sections, we will provide a detailed comparison of DeepSeek R1 and Manus AI, covering their technical architecture, training methods, performance, applications, strengths, weaknesses, and prospects.

Technical Architecture

The key difference between DeepSeek R1 and Manus AI is their design philosophy. DeepSeek R1 is a large language model, which is a single neural network that processes text input and generates text output. In contrast, Manus AI is a multi-component AI system made up of several cooperating modules, using external tools to achieve a goal. DeepSeek R1 focuses on the neural network’s structure and parameters, while Manus AI emphasizes the organization of multiple AI models and tool interfaces within the system.

DeepSeek R1 Architecture

DeepSeek R1 is based on the transformer-based LLM paradigm with innovations to enhance reasoning performance and efficiency. It uses a Mixture-of-Experts (MoE) architecture, dividing the model into many expert subnetworks. Only a subset of these experts is activated for each input, which allows for a high parameter count with manageable computation per token. The model has 671 billion total parameters, with about 37 billion active per inference. Essentially, DeepSeek R1 functions like a committee of expert networks, with only a few used for each query. This design boosts knowledge capacity without increasing computer costs. DeepSeek’s MoE system employs routing and load-balancing mechanisms to ensure expert selection and prevent overloads. This expert selection enhances computational efficiency and promotes specialization in different reasoning tasks within the model’s layers.

DeepSeek R1’s architecture is based on transformer layers like other LLMs such as GPT or LLaMa families, but it includes MoE layers. The creators offer various model sizes, including a large expert-filled model, and smaller dense models with different parameters (70B, 32B, 14B, 8B, 7B, and 1.5B). The creators derived these smaller models from existing architectures like Meta’s Llama and Alibaba’s Qwen, creating standalone versions of DeepSeek R1 for easier deployment. The largest distilled variant, DeepSeek-R1-Distill-Qwen-32B, has set a new standard as the top-performing “dense” model, surpassing a smaller OpenAI reference model in various benchmarks.

DeepSeek’s architecture is efficient due to MoE, enabling it to scale to hundreds of billions of parameters without using every parameter for every token. This makes it faster and requires less memory than a dense model of the same size during inference. The project has optimized DeepSeek R1 through techniques like quantization, offering 8-bit versions that can run on common hardware despite its large size. DeepSeek R1 is an advanced LLM that boosts scale and reasoning ability through an innovative expert-model design in a single interface.

Optimizing AI Systems with Mixture of Experts (MOE): DeepSeek and Beyond

Manus AI Architecture

Manus AI has a one-of-a-kind architecture. It focuses on orchestrating agent behavior rather than relying on a single network. Manus comprises several AI components working together. It follows an Executor–Planner–Knowledge agent paradigm. The Executor agent interacts with the user and carries out steps. Behind it, Manus has a planner agent that plans actions and a knowledge agent that handles queries or expertise. This separation means the Executor doesn’t have all the knowledge or plan everything; it delegates and asks. Manus’s chief scientist Yichao “Peak” Ji explains that this multi-agent system helps manage complexity. Each agent focuses on its task subset, and the Executor only sees summaries of what it needs. For example, if a task needs information, the Executor asks the Knowledge agent for facts instead of storing all conversation history.

Manus AI uses existing large language models as the brains for its agents. It doesn’t create new transformer networks, but incorporates powerful existing models. In its current version (early 2025), Manus uses Anthropic’s Claude 3.5 and fine-tuned versions of Alibaba’s Qwen model. Claude is the reasoning engine, while Qwen variants handle specific tasks or reduce reliance on one provider. Users interact with the Executor agent, which uses Claude or Qwen models to fulfill requests. Manus offers 29 tools/APIs for agents to use, including web browsing, search, code execution, and data visualization. The agent loop involves the Planner deciding the next step, the Executor using a Browser tool, the Knowledge agent providing an answer, and Manus proceeding iteratively.

The Manus architecture focuses on modularity and sandboxing. The system performs each action in a controlled environment for safety. This setup enables the AI to do various tasks while keeping them separate from the core model for security. The designers created Manus as a “general AI agent that turns thoughts into actions” by integrating planning, tool use, and model queries. It can enhance its capabilities by upgrading models or adding tools without overhauling the entire system. The team is testing Claude 3.7 and plans to integrate improved models. The architecture prioritizes orchestrating existing AI components, rather than introducing new neural network innovations. Manus AI’s architecture resembles a software agent with an AI “mind”, composed of multiple LLM instances and a toolbox, managed through a loop of planning and execution steps.

Training Methodologies

DeepSeek R1 and Manus AI have different training methods and architectures. Researchers trained DeepSeek R1 from scratch using reinforcement learning techniques. Researchers created Manus AI by combining pre-trained models and engineering their interactions with minimal new model training.

DeepSeek R1 Training Approach

DeepSeek R1’s development focused on training large language models using reinforcement learning (RL) to improve reasoning skills. The team first created DeepSeek-R1-Zero, a model trained with RL without supervised fine-tuning (SFT). They trained this model directly in an environment where it had to solve tasks and receive rewards. This approach differed from traditional methods that start with massive supervised training on human-written Q&A or instructions. DeepSeek-R1-Zero showed emergent reasoning behaviors, such as self-reflection and self-verification of answers learned solely through RL. This achievement marked the first evidence that a language model can enhance reasoning skills through reinforcement learning without extensive exposure to human examples.

R1-Zero had issues such as repetition, poor readability, and language mixing. It could reason, but struggled to communicate clearly. To address this, the DeepSeek team introduced DeepSeek-R1, a refined model with a multi-stage training pipeline. They added supervised training steps alongside RL and included cold-start data before RL. DeepSeek-R1’s pipeline had two SFT stages and two RL stages. One RL stage focused on improving reasoning patterns, while the other aimed to align with human preferences. This likely involved Reinforcement Learning from Human Feedback (RLHF) to enhance the model’s outputs. DeepSeek R1 combined strong reasoning skills with improved coherence and usefulness.

The training of DeepSeek R1 was highly cost-effective, being 90-95% cheaper than expected for similar models. They achieved this by using RL efficiently, employing a Mixture-of-Experts architecture, and utilizing distillation to compress knowledge. These methods allowed them to train a top-tier reasoning model with minimal resources and without high-end hardware. The DeepSeek team demonstrated that the right training approach is more important than expensive AI chips in achieving remarkable results.

It’s worth noting the collaborative nature of DeepSeek R1’s creation: with nearly 200 authors on the arXiv paper, it appears to have been a massive community or multi-organization effort, possibly pooling expertise and computer. The training likely involved large-scale computing clusters, but perhaps not only the highest-end GPUs, which also speaks to the accessibility. Now that it’s trained, DeepSeek R1 is openly available, and its distilled versions can be fine-tuned by users on their data at much lower cost, extending the training method into continual learning by the community.

Manus AI Development Approach

The team created Manus AI through integration, fine-tuning, and system design, rather than training a new model from scratch. The team used existing LLMs like Anthropic’s Claude and Alibaba’s Qwen to build their autonomous agent. By doing this, they saved time and data required for training. Manus is likely fine-tuned Claude and Qwen for their specific needs, such as teaching Qwen how to interact with tools or plan steps. They may have also created smaller utility models, like a tool selection classifier or a short-term memory summarized. The pre-existing AI models handled the core language understanding task.

Manus’s method involves prompt engineering and few-shot orchestration. Instead of training the entire agent end-to-end with gradient descent, the developers created a loop where agents communicate. They focused on designing prompts for the Planner agent, the format for the Knowledge agent’s information, and how the Executor summarizes results. Jian Liao’s investigation revealed Manus’s transparent coordination script and runtime system prompts. The Manus team can improve agent performance by testing it on various scenarios and adjusting prompts instead of training neural network weights. This method is more about programming an AI workflow than training an AI brain, representing a unique development approach.

Manus AI’s development used fewer computer resources compared to DeepSeek. The main tasks involved were fine-tuning models like Qwen, which could take a few hundred GPU-hours, and running trial episodes of the agent on benchmark tasks. The team prioritized open-source and existing technologies, recognizing their reliance on community tools like the Browser tool (an open-source project called browser-use) and open libraries for tasks such as running code or querying databases. They also aim to give back to the open-source community.

Manus’s creation was cost-efficient in development, but running the agent can be computationally heavy because of many calls to large models and executing code. The innovation in Manus lies in system design, not just model training. It challenges the idea that breakthroughs require training a giant new model. Instead, Manus built an agent that avoids training a massive proprietary model from scratch. This approach combines existing models and agents to create a highly capable system. People consider Manus a potential “DeepSeek moment” for agentic AI, showing that a small startup can rival big labs in agent capability. The training and development focus on orchestration and fine-tuning rather than giant-scale training, leading to Manus’s impressive early performance.

Performance Benchmarks

To evaluate DeepSeek R1 and Manus AI, we need to consider different benchmarks, as one is a standalone model and the other is an autonomous agent. We can test both models on complex tasks. We will compare their performance on industry-standard benchmarks like GAIA, MMLU, GSM8K, coding tasks, and more. This comparison will help us understand their strengths in different domains.

GAIA Benchmark (General AI Agents)

The GAIA benchmark tests AI systems on tasks that need reasoning, tool use, and multi-step problem solving. Manus AI is designed as an autonomous agent, making GAIA a perfect test for it. DeepSeek R1, an LLM without tool-using capability, is not suitable for GAIA tasks as it can answer questions but cannot use tools. Thus, GAIA is most relevant to Manus AI.

Manus AI excelled in GAIA evaluations for AI agents, surpassing previous state-of-the-art systems. The evaluations covered tasks of varying difficulty levels - basic, intermediate, and complex. Manus achieved 86.5% success on basic tasks, outperforming OpenAI’s agent which scored around 74.3%. On intermediate tasks, Manus scored 70.1%, slightly higher than OpenAI’s 69.1%. Even on the most complex tasks, Manus led with 57.7% success compared to OpenAI’s 47.6%. These results from Manus AI team’s reports show Manus’ superiority in the agent benchmark. In practical terms, tasks could involve activities like planning a vacation or analyzing financial records. Manus’ performance suggests it is more dependable and efficient to complete such tasks by executing necessary steps effectively.

GAIA measures various abilities like understanding instructions, recalling knowledge, using tools, and staying on task through multiple steps. Manus excels in coordinated autonomy. DeepSeek R1 alone cannot operate a browser or produce a conclusive answer without being connected to an agent framework. Comparing DeepSeek’s percentage score on GAIA is not possible, but Manus stands out in this category because of its design.

Knowledge and Reasoning (MMLU and Academic Benchmarks)

DeepSeek R1 performs well on traditional NLP benchmarks, such as MMLU, where it achieved about 90.8% accuracy in English. This is comparable to top closed models like the one from OpenAI, scoring 91.8%. DeepSeek R1’s performance indicates it has matched the leader in knowledge and reasoning. It outperforms earlier models like GPT-3.5 and LLaMA-2, which scored in the 70-80% range on MMLU. Even outscoring the March-2023 version of GPT-4, which was around 86-87% in some reports. In the more challenging MMLU-Pro variant, DeepSeek R1 also scored well at 84.0%. These results show that DeepSeek R1 excels in language understanding and factual recall, making it highly reliable for answering questions across different subjects.

Manus AI’s performance on MMLU depends on its underlying language model, as MMLU is a Q&A task without complex planning. Manus currently uses Claude 3.5, which scored 88.3% on MMLU, slightly lower than DeepSeek R1’s 90.8%. DeepSeek may answer a few more questions correctly on average. Manus could use tools to search for answers it doesn’t know, but MMLU is usually a closed-book test. DeepSeek R1 likely has an advantage in pure knowledge tasks due to its extensive training and larger model size. Manus defeated an “OpenAI Deep Research” agent using an OpenAI “o3” model, suggesting Manus’s integration and planning gave it an edge in tasks requiring reasoning, even if its base model wasn’t the best at recalling facts.

Mathematical Reasoning (GSM8K, MATH, and Logic Puzzles)

Mathematical problem-solving is important for measuring a model’s reasoning depth. GSM8K is a benchmark for middle school math word problems that require careful step-by-step reasoning. DeepSeek R1 has exceptional mathematical reasoning capabilities, comparable to the best models. DeepSeek R1 achieved a remarkable 97.3% accuracy on the MATH dataset, solving almost all problems correctly. In comparison, OpenAI’s top model scored about 96.4% on the same dataset. Models before 2024 struggled with GSM8K, even GPT-4 had difficulties. DeepSeek R1’s training via RL likely honed its chain-of-thought ability to an extreme degree, explaining its success.

Manus AI’s math problem performance depends on Claude’s abilities and possibly tool use. Claude 3.5 performed well on GSM8K but not as well as GPT-4, which was the first model to exceed 80% accuracy on GSM8K. DeepSeek R1 matches GPT-4 in math, suggesting it would outperform Manus’s base model in direct math Q&A. Manus could improve its math problem-solving by doing calculations or writing a Python script to compute answers. This method can sometimes overcome the limitations of the language model’s mental math. In a scenario where Manus can use a calculator or code, it may solve 100% of GSM8K through brute force, while DeepSeek does it mentally. Without explicit tool use, Manus’s Claude brain might be slightly less skilled than DeepSeek. DeepSeek’s reinforcement approach targeted reasoning explicitly, unlike Manus’s Claude model, which was not solely optimized for math during training.

Coding Tasks

Coding ability is an important benchmark for AI. DeepSeek R1 excels in this area. In the Codeforces benchmark, it achieved a rating of 2029, close to OpenAI’s model at 2061, showing an expert level of coding proficiency. Even though GPT-4 had a higher rating when allowed multiple attempts, DeepSeek R1 shows strong performance under strict conditions. Additionally, DeepSeek R1 had a success rate of about 65.9% on LiveCodeBench, outperforming the OpenAI model. This shows R1’s strong capability in writing correct code for complex tasks, attributed to its training on code and advanced reasoning abilities.

The literature lacks benchmarks of Manus AI on coding challenges like HumanEval or Codeforces. However, one of its impressive abilities is autonomously generating and deploying code and applications. For instance, the Manus team showcased its capability by creating a full working dashboard web app from a single prompt without additional human input. This shows Manus’s high coding proficiency as it can write, run, and deploy code. In practical coding situations, Manus may have an edge as it can execute and debug the code it generates, similar to human programmers. This iterative process sets Manus apart from static models like DeepSeek, which require manual intervention to run and correct errors. Therefore, in interactive coding tasks, Manus could achieve near-perfect outcomes through trial and error.

On pure coding benchmarks, DeepSeek R1 excels with strong scores in raw output quality. Claude 3.5, which is based on Manus’s core, is a decent coder but may not match up to GPT-4 or possibly DeepSeek to code prowess. For instance, in the data we observed, Claude’s Codeforces rating was 717, whereas DeepSeek’s was 2029. If we compare these metrics directly, it suggests that DeepSeek’s coding output is much more precise with no testing. However, Manus can test and refine its output, which might help address any initial inaccuracies.

DeepSeek R1 excels in knowledge, reasoning, math, and coding benchmarks, outperforming top models like OpenAI. Manus AI surpasses other agents in multi-step task execution on the GAIA benchmark. Manus’s performance in question-answering and coding is on par with models like Claude but slightly below DeepSeek. However, Manus stands out in applied tasks by leveraging tools and iterative refinement.

Benchmarks only show part of the story. Controlled benchmarks can be impressive, but real-world unpredictability may lead to different results. Manus shows promise, but users sometimes have trouble with the product. DeepSeek’s benchmark dominance shows its raw capability, but integrating it into applications for end-to-end tasks may need additional system engineering, where Manus has an advantage.

Real-World Applications

DeepSeek R1 is a powerful language model used as an AI engine for tasks involving text or code. Manus AI, an autonomous agent, automates complex workflows without human intervention. Both technologies serve specific use cases in research, business, and automation.

DeepSeek R1 is a versatile AI model known for its strong reasoning and coding abilities. It is popular among researchers and developers as an open alternative to models like GPT-4. In scientific research or academic work, DeepSeek functions as an intelligent assistant that can analyze research papers, answer domain-specific questions, and help draft literature reviews by synthesizing information.

DeepSeek R1 is reliable for fact-checking and deep Q&A in fields like law or medicine because of its high precision on complex questions. It’s open-source and developers have integrated it into various applications and platforms. For instance, Amazon Web Services (AWS) offers DeepSeek-R1 on Amazon Bedrock, their AI model hosting service.

This integration allows businesses to deploy DeepSeek via AWS easily to power their internal solutions, such as analytics dashboards accepting natural language queries or customer support chatbots handling complicated customer issues. AWS highlights that with DeepSeek-R1, customers can solve complex problems, write code, crunch numbers, and more by connecting to its API.

DeepSeek R1 in software development acts as a coding copilot, generating code snippets or functions based on descriptions. Trained with a focus on reasoning, it can understand code intent and debug logic, helping identify errors or propose enhancements. Smaller models are available for local use, allowing individual developers or small startups to access DeepSeek without needing cloud GPUs. These models are used to automate tasks like writing unit tests, converting programming languages, or optimizing algorithms in the community.

One emerging application area is data analysis and business intelligence. DeepSeek can handle multi-step reasoning. This allows a company to analyze a large dataset or report and generate insights in plain English. For instance, an analyst could ask why sales dropped in the last quarter. DeepSeek R1 can then parse through the data and provide a reasoned explanation or a step-by-step analysis. While DeepSeek is a text model, it can work with databases or tools to perform tasks. DeepSeek R1 is a brainy component in larger systems, meant to think and generate content that other software can act upon.

Manus AI is a digital autonomous assistant that acts on your behalf, like an executive assistant. It can help with tasks like planning a trip, where you can tell it what you need and it will find options within your budget. For example, if you ask Manus to plan a trip to New Zealand under $2,000, it will search for flights, find hotels, compare prices, and possibly make bookings. This service goes beyond just giving suggestions; it aims to provide a completed itinerary. In e-commerce, Manus can help with tasks like finding items at the best prices by searching shopping sites and completing checkout processes automatically.

Manus AI has the potential to automate tasks involving cross-software operations in business. For instance, in a Human Resources department, Manus can screen resumes, rank candidates, and schedule interviews autonomously with just one instruction. Reports indicate Manus is used for screening job applicants and analyzing resumes.

In finance, Manus AI can conduct routine financial analysis by identifying trends or anomalies in transaction data, preparing summary reports, and creating visualizations like charts or dashboards. DataCamp mentions examples such as analyzing financial transactions and searching for rental properties as tasks Manus can perform.

A practical use case for Manus is in real estate search. A user can ask Manus to find the best rental property in a specific city based on set criteria. Manus will search listing websites, filter results, contact landlords if necessary, and provide a concise report of top options, saving significant time compared to a human assistant.

Manus can act as a DevOps or IT automation agent in tech-related settings. For example, a software engineer could ask Manus to deploy a new microservice to the cloud environment with specific uptime requirements. Manus would handle tasks like reading documentation, executing shell commands, setting variables, testing the deployment, and notifying the user. This automation can significantly boost productivity in such scenarios.

The DeepSeek R1 model is good for tutoring students in math and helping writers with story plots. The Manus AI model can be an AI research assistant by finding sources, collecting information, and making draft reports or slideshows. For instance, Manus can create a data dashboard from a prompt, showing its ability for automated research presentations.

Manus AI is used to offload entire tasks, especially in multi-step processes like booking, purchasing, data entry, analysis, scheduling, or cross-application workflows. DeepSeek R1 is used for deep text or code understanding and generation in chatbots, assistants, coding tools, and analysis apps. These two tools can complement each other, with DeepSeek R1 used within an agent like Manus as the reasoning engine. The concept of “moments” is converging, with DeepSeek showcasing open models’ power and Manus applying it in an agent form. It may not be surprising if some users run a Manus-like agent but switch to DeepSeek R1 as the model instead of Claude to combine both strengths.

Strengths and Weaknesses

DeepSeek R1 and Manus AI each have unique strengths and weaknesses. We will analyze their key advantages and drawbacks using empirical data and user feedback.

DeepSeek R1 – Strengths

DeepSeek R1 demonstrates exceptional reasoning performance by achieving top-tier results on various benchmarks. It excels in tasks involving complex reasoning, math, and knowledge. For instance, it scores approximately 90% on MMLU and around 97% on advanced math problems, showcasing its remarkable ability to solve challenging questions accurately. This positions DeepSeek R1 as one of the most advanced LLMs, performing on par with or even outperforming models like GPT-4 in problem-solving abilities.

DeepSeek R1 is open source and accessible to the research and developer community. The model and its variants are open-sourced, allowing anyone to use or fine-tune them. This democratizes access to a powerful AI. The team also released distilled models ranging from 1.5B to 70B parameters, making it easier to run the model on moderate hardware. This openness contrasts with many top models that are closed or behind APIs. It encourages innovation, enabling developers to integrate DeepSeek freely into their own projects.

DeepSeek R1 is a cost-effective and efficient tool that performs well without requiring a large budget. Training costs are reportedly 90–95% more cost-effective than similar efforts. It uses a Mixture-of-Experts architecture to make inference efficient by activating only a subset of the model’s parameters per query, reducing runtime costs. Deployment is also efficient, with quantized models and partnership with AWS Bedrock for serverless scaling, making DeepSeek more affordable for businesses and researchers.

Innovative Training method: Using reinforcement learning to enhance reasoning directly is a new and successful approach. DeepSeek R1 is the first open model to show that RL alone can lead to complex reasoning behaviors. This is a significant strength for research, as it paves the way for new AI training methods beyond standard text corpora pretraining. The multi-stage RL+SFT pipeline refines the model in ways that pretrained models cannot achieve. As a result, DeepSeek R1 is aligned with human-like solutions and preferences because of its training choices, potentially making it more reliable in reasoning tasks.

• Versatility (Math, Code, Multilingual): DeepSeek R1 is a generalist in terms of capabilities. It performs strongly not just in English but in other languages (for instance; it achieved ~92% on the Chinese C-Eval benchmark , showing high multilingual ability). It’s also proficient in coding tasks, which opens up usage in software development support. This versatility is an advantage, as one model can apply to many tasks. Users have found that DeepSeek can handle logical puzzles, generate well-structured essays, solve programming challenges, and more, without specialized tuning for each – a testament to its broad training and reasoning skill.

DeepSeek R1 – Weaknesses

Initial Output Quality Issues (now mitigated): The first version, DeepSeek-R1-Zero, while a proof of concept for RL-based reasoning, had issues with output quality – “endless repetition, poor readability, and language mixing” were observed. Even though the final DeepSeek R1 addressed many of these issues with additional training, some quirks may remain. Users might still occasionally find that DeepSeek’s answers are overly verbose or follow unusual chain-of-thought styles because of its RL background. Essentially, the model’s communication style had to be corrected via fine-tuning; it wasn’t naturally smooth out of the gate.

Operational Challenges (Deployment and Latency): As an extremely large model (with a MoE architecture), running DeepSeek R1 in real-time can be challenging. The full model (with experts) is heavy–although only 37B params are active at a time, coordinating the experts across potentially many devices can introduce complexity. In the early days of release, DeepSeek’s servers struggled with the load of user requests. This shows that practical deployment at scale needs robust infrastructure. For users without access to services like AWS Bedrock, hosting the model themselves might be non-trivial, especially the MoE version. There can be latency in responses if the model or its context window is very large. The distilled models mitigate this, but then one trades off some accuracy.

Incomplete Alignment and Knowledge Gaps: While very advanced, DeepSeek R1 is not infallible. It still can hallucinate (produce incorrect facts) if asked obscure questions, as any LLM can. AWS’s guidance even recommends using guardrails to detect and prevent hallucinations when deploying it. Its alignment with human intent, though improved by RLHF, might not equal the level of fine-tuned proprietary models that underwent extensive safety training. Users have to be cautious with its outputs in critical applications–e.g., it might use complex reasoning but arrive at a subtly wrong answer that sounds convincing. Furthermore, its heavy training on reasoning tasks might make it slightly less optimized for straightforward Q&A or casual conversation. (Indeed, on the Simple QA benchmark, DeepSeek underperformed OpenAI’s model, possibly because it overthinks simple questions or lacked specific tuning for them.)

Resource Requirements: Even though DeepSeek is cost-efficient relative to peers, it’s still a large model requiring significant compute to train and run. Not every organization can do an RL training run like DeepSeek’s on their data–it’s still a high barrier if starting from scratch (though they can use the pre-trained model). Fine-tuning the 70B or even 32B distilled model needs powerful GPUs and memory. And while quantized versions exist, using the model with a very long context or in an interactive application requires careful system design. In short, it’s not a lightweight AI; it’s a behemoth (just an open and clever one).

Lack of Tool Use/Internal Knowledge Update: Unlike an agent, DeepSeek R1 on its own cannot use tools or browse the internet when it doesn’t know something. We also fixed its knowledge cutoff (likely in 2024, given its training date). So if asked about events after its training or if it needs to use a calculator or database, it won’t have that ability unless a developer explicitly hooks it up to those tools. This inherent limitation stems from model constraints. Manus can extend beyond its trained knowledge by searching or executing code. DeepSeek would need a similar wrapper to do that, so out-of-the-box it’s limited to what it internalized during training.

Please read: Focus on AI Innovation in Banking: Highlights MoE as a transformative technology for real-time Transaction monitoring