In this article DeepSeek vs Gemini, we are going to explore the following topics:
What is DeepSeek and its hedge fund background
DeepSeek's capabilities and model variations
Gemini's features and different model options
Cost comparison between both platforms
Performance benchmarks in key areas
Head-to-head tests in:
Image analysis capabilities
Document handling
Coding performance
Web search functionality
Real user experiences from Reddit
Elephas - a tool that combines both models' powers
A detailed breakdown to help you choose the right model
By the end of this article, you'll understand exactly how these AI models compare and which one best suits your needs.
Let's get into it.
What is DeepSeek?
DeepSeek is a powerful AI system that offers capabilities similar to other leading AI chatbots, but with a unique twist - it's completely free and open source. Built on the impressive R1 model with 670 billion parameters, it stands as the largest open-source language model available today.
Interestingly, DeepSeek isn't just another AI company. Founded in December 2023 by Liang Wenfeng, it's actually a side project of High-Flyer, a successful hedge fund that uses AI for investment decisions.
This unusual background gives DeepSeek a different perspective, combining advanced AI technology with real-world financial expertise to create a unique and powerful AI tool.
DeepSeek Capabilities
DeepSeek V3 shows impressive performance across various areas of artificial intelligence, as demonstrated by its benchmark scores. Looking at the data, DeepSeek V3 stands out with a strong global average score of 60.4, placing it among the top-performing AI models.
Key Performance Areas:
Intelligence Factor (IF): DeepSeek V3 excels with an outstanding IF Average of 80.9, showcasing its advanced problem-solving abilities and overall intelligence capabilities
Reasoning Skills: The model achieves a solid score of 50 in reasoning tasks, demonstrating its ability to process and analyze complex information
Coding Proficiency: With a score of 63.4 in coding tasks, DeepSeek V3 shows strong capabilities in programming and code generation
Data Analysis: The model performs well in data processing with a score of 57.7, indicating its effectiveness in handling and analyzing various types of data
Performance Breakdown:
Mathematics capabilities reach a score of 60, showing competency in handling mathematical problems and calculations. The language processing abilities score 50.2, indicating its capacity to understand and work with different languages effectively.
When compared to other models in the benchmark, DeepSeek V3 maintains competitive performance across all categories, positioning itself as a well-rounded AI system. Its balanced scores across different areas make it particularly suitable for diverse applications, from technical tasks to general problem-solving scenarios.
Different DeepSeek Models
DeepSeek has developed a range of AI models, each designed to meet specific needs while balancing performance and efficiency. The company's approach focuses on creating both powerful main models and smaller, more efficient versions that maintain strong capabilities.
Main Model Families:
Qwen-Based Models
The 1.5B version excels in fundamental mathematical operations
Their 7B model provides a sweet spot between math skills and general knowledge
The 14B variant offers enhanced capabilities in complex problem-solving
Their flagship 32B model stands out in advanced mathematical applications
Llama-Based Series
The compact 8B model delivers efficient performance across multiple tasks
Their advanced 70B version competes with industry leaders in overall capabilities
Performance Highlights:
Mathematical Excellence: The models show particularly strong performance in mathematical tasks, with the Llama-70B version achieving outstanding scores in complex calculations
Resource Efficiency: Each model is optimized to provide maximum performance while managing computational requirements effectively
Versatile Applications: The range of models supports various use cases, from basic tasks to advanced applications
This lineup allows organizations and developers to select models that best match their specific requirements, whether they need lightweight solutions for simple tasks or powerful models for complex applications.
DeepSeek Pricing
DeepSeek is completely free to use, and you can even download any one of their models according to your needs and run the model on your device.
However, if you want to use DeepSeek API, the costs are as follows
What makes DeepSeek Different?
DeepSeek stands out in the AI world by offering something unique - it's open source, meaning anyone can use and modify it freely, unlike many other AI models that require payment. This openness hasn't compromised its quality though, as DeepSeek can go head-to-head with premium AI models like OpenAI's Claude and GPT-4.
What's particularly impressive about DeepSeek is its ability to think and reason through complex problems. The model shows strong performance in areas like mathematics, coding, and general problem-solving. Its thinking process feels natural and logical, helping it tackle challenging tasks effectively.
DeepSeek vs Gemini Cost Comparison
The graph compares MMLU-Pro scores with inference price per million tokens. A higher MMLU-Pro score indicates better performance, while a lower inference price means a more cost-efficient model. DeepSeek-V3 and Gemini-1.5-Pro are both positioned in the high-performance range, but DeepSeek appears to offer better cost efficiency compared to Gemini. Gemini-1.5-Pro is priced higher while offering slightly better performance, making DeepSeek the more balanced choice for affordability.
This graph presents LiveBench Global Average Performance Score vs. price per million tokens. DeepSeek-V3 and Gemini-2.0-Flash score similarly in terms of intelligence. However, Gemini models tend to have a higher price per million tokens, making DeepSeek a more budget-friendly option.
DeepSeek is the better choice for those looking for a balance between performance and cost, while Gemini is suited for users prioritizing advanced intelligence despite the higher price.
What is Gemini?
Gemini is Google's advanced AI chatbot that can work with different types of content like text, images, audio, and videos all at once. Unlike earlier AI systems that handled different types of content separately, Gemini learns everything together from the start, helping it understand things more naturally and deeply.
Gemini Capabilities
Math Excellence: Gemini shows impressive performance in mathematical tasks, scoring over 90% in challenging math problems including algebra, geometry, and calculus. This makes it particularly strong for advanced mathematical applications.
Strong Multilingual Skills: The system performs exceptionally well in handling multiple languages, with Gemini 2.0 Pro achieving an 86.5% score in global language understanding across 15 different languages.
Factual Knowledge: Gemini demonstrates solid performance in fact-based tasks, with its FACTS Grounding score reaching 82.8%, showing a strong ability to provide accurate information when given documents and user requests.
Key Performance Areas:
Code Generation: Achieves 36% accuracy in Python code generation, showing competence in programming tasks
Reasoning: Scores 64.7% in complex scientific questions across biology, physics, and chemistry
Image Understanding: Reaches 72.7% accuracy in processing and understanding various types of images
Audio Processing: Shows steady improvement in speech translation with a score of 40.6
Video Analysis: Maintains consistent performance of 71.9% in analyzing videos across different domains
Different Gemini Models
Gemini comes in three main models, each designed for different needs and uses. These models include Gemini 2.0 Flash, Gemini 2.0 Flash-Lite, and Gemini 1.5 Flash. Each model has its own strengths - 2.0 Flash offers next-generation features with improved capabilities, Flash-Lite focuses on cost efficiency and low latency, while 1.5 Flash provides balanced performance across most tasks.
For those interested in using Gemini's API, you can explore all available models at google.dev/gemini-api
However, through the Gemini interface from gemini.google.com, you can access Gemini 2.0 Flash and its experimental thinking variants. These versions are specifically designed for direct user interaction and come with enhanced features for everyday use.
Gemini Pricing
To use Gemini Advanced, users have to pay $20/month, and if you want to use the Gemini API, then the API costs are as follows
What makes Gemini Different?
What makes Gemini special is its ability to process large amounts of information at once. The latest version, Gemini 2.o flash thinking, the model is particularly notable for its extensive context window, which supports up to 1 million tokens for input and allows for responses of up to 64,000 tokens.
This feature significantly improves the model's ability to handle long-form text and complex arguments without losing coherence. This means you can share long documents, multiple images, or complex problems with it, and it will understand and work with all of that information together.
But the biggest difference between Gemini and other models is that it can integrate with other Google services such as Gmail, Docs, YouTube, etc. Especially the 2.0 flash thinking experiment with apps model that was specifically created to use Gemini with other Google services.
Redditors Opnion on DeepSeek vs Gemini
In any discussion about AI models, the Reddit community's hands-on experiences and candid feedback is a must. A recent Reddit thread in r/OpenAI sparked an engaging discussion about Google's Flash 2.0, DeepSeek R1, and OpenAI's models.
Let’s look into the thread.
Model-Specific Strengths and Comparisons
Several users provided balanced perspectives on each model's strengths. Mikethespike056 observed that while Flash 2.0 excelled in providing detailed responses, DeepSeek R1 remained superior for coding tasks. They also praised O3-mini-medium for delivering well-structured explanations and maintaining consistent markdown formatting that enhanced readability.
Technical Capabilities and Context Window
User sdmat emphasized Flash 2.0's exceptional price-to-performance ratio and its impressive long context window capabilities. Meanwhile, katonda shared their journey from being initially skeptical of Gemini in December to becoming enthusiastic about its improved 2.0 version, particularly noting its positive impact on their productivity.
Critical Analysis and Caution
LiteratureMaximum125 offered a more critical perspective, cautioning against overgeneralization based on limited testing. They advocated for more specific and targeted conclusions about each model's capabilities rather than broad claims of superiority.
Performance and Cost Trade-offs
Some users like Svetlash123 maintained that while Gemini had made significant progress, it still lagged behind top O1 and O3-mini models in terms of performance, particularly in coding and reasoning tasks. However, strangescript found Flash 2.0 to be equivalent to competitors while offering better speed and cost benefits.
Notable Technical Features
The thread particularly highlighted Flash 2.0's million-token context window capability, with uzi_loogies_ emphasizing how this feature set it apart from many competitors. This sparked further discussion about the practical implications of such extensive context handling.
Comparing DeepSeek vs Gemini: Which is best?
To know which is better in practical use cases, we tested both models, and some tests we did with DeepSeek vs ChatGPT are also done with Gemini too. But it has been quite a while since we did that article, and let's see if we got any new changes in DeepSeek. For these tests, we are going to use the Gemini 2.0 experimental with apps.
Test 1: Image Analyzation
Image credit: Behance
In this test, we have given both models the above image and asked them to provide an estimation of how many words are in the image and also to find the word “Moment” in the image. Gemini was actually able to answer the question and even found the word in the image.
But DeepSeek gave a completely wrong answer and even said there is no “Moment” word in the image. However, comparatively, the response is much better than when we tried the model in the recent tests for ChatGPT vs DeepSeek, but still, it cannot give the right answer.
Test 2: Document Analyzation
Next, we try to test the document analysis capabilities of both models. We provided a research paper PDF to both models and asked for a summary and the reference name in the paper. Gemini wasn't even able to take the PDF as an input and showed an error.
In contrast, DeepSeek was able to analyze the PDF, provided a good summary, and also identified the author names in the paper.
Test 3: Coding
For the next test, we asked both models to generate a Flappy Bird game that can run in an online compiler. Gemini provided the code in C++, and DeepSeek in Python. However, the Gemini code gave errors, while the DeepSeek code executed successfully, allowing us to play the game.
Although the game is not completely playable because it gets restarted every 5 seconds, compared to Gemini, DeepSeek was able to generate the code.
Test 4: Web Search
For the final test, we asked both models about the Paris AI summit. DeepSeek completely stated that the year was wrong in its thinking description and gave output as its knowledge is cut off at July 2024.
However, I actually enabled the web search feature, and even after web searching, the model provided a knowledge cut-off output. Gemini gave the answer as expected.
If you also want to know the latest AI news of the week, such as the AI summit, without any fluff and in a simpler and more digestible format than Gemini, subscribe to our newsletter.
Note: DeepSeek model is hard to use because only after 3 to 4 messages we get the server busy, and I have to use different accounts to perform these tests.
Elephas: Best Mac AI Assistant
Elephas brings together the best of both worlds by letting you use DeepSeek and Gemini, along with other AI models, all in one place on your Mac. Elephas is best for Mac-based knowledge management and local AI workflows. It helps you work smarter and faster, saving 10 hours a week.
Key Benefits:
Switch freely between different AI models including DeepSeek, Gemini, Claude, and OpenAI to get the best results for your specific needs
Build your own knowledge base with the Super Brain feature that lets you save and organize information from anywhere
Work completely offline with local AI capabilities, keeping your data private and secure
Handle various file types including CSV, JSON, and many more, making it easy to work with all your documents
Export your conversations in different formats like PDF, MD, and TXT for easy sharing and reference
It also has different writing features that help you in day-to-day writing tasks.
DeepSeek vs Gemini: The best choice?
When comparing DeepSeek and Gemini, each model offers distinct advantages that make them suitable for different needs. DeepSeek stands out with its open-source nature and cost-effectiveness, while Gemini excels in its integration with Google services and advanced features.
Performance-wise, both models show strong capabilities. Gemini scores higher in mathematics (90%) and multilingual tasks (86.5%), while DeepSeek demonstrates solid performance in coding (63.4%) and overall intelligence (IF score of 80.9).
Gemini's 2.0 Flash thinking model offers a million-token context window, enabling better handling of long documents and complex tasks.
In practical tests, each model has its strengths. Gemini performs better in image analysis and web searches, staying current with recent events.
DeepSeek, however, shows superior performance in document analysis and coding tasks, successfully handling PDFs and generating working code.
Cost considerations favour DeepSeek, as it's free and open-source with reasonable API pricing. Gemini requires a $20 monthly subscription for advanced features and has higher API costs.
The choice between them depends on specific needs: Choose Gemini if you need Google service integration and strong multimedia capabilities, or opt for DeepSeek if you prioritize cost-effectiveness and powerful coding capabilities.
FAQs
1. Is DeepSeek better than Gemini for coding tasks?
Yes, DeepSeek generally performs better in coding tasks. In practical tests, DeepSeek successfully generated working Python code for a Flappy Bird game, while Gemini's C++ code produced errors. DeepSeek also shows stronger capabilities in code analysis and generation.
2. Which is more cost-effective, DeepSeek or Gemini?
DeepSeek is more cost-effective as it's free and open-source with reasonable API pricing. Gemini requires a $20 monthly subscription for advanced features and has higher API costs, making DeepSeek the more budget-friendly option.
3. Can I use both DeepSeek and Gemini together?
Yes, you can use both DeepSeek and Gemini through Elephas, a Mac AI assistant. Elephas lets you switch between different AI models and combine their capabilities while managing your knowledge base locally.
4. Which AI model is better for image analysis?
Gemini performs better in image analysis tasks. During testing, Gemini accurately analyzed images and identified specific words, while DeepSeek struggled with image comprehension and provided incorrect responses to image-based queries.
5. Does DeepSeek work offline?
Yes, DeepSeek can work offline as it's open-source. You can download any of their models and run them locally on your device, making it ideal for users who need AI capabilities without constant internet connectivity.
