When DeepSeek R1 launched in January, it instantly became one of the most talked-about open-source models on the scene, gaining popularity for its sharp reasoning and impressive performance. Fast-forward to today, and DeepSeek is back with a so-called “minor trial upgrade”, but don’t let the modest name fool you. DeepSeek-R1-0528 delivers major leaps in reasoning, code generation, and overall reliability. With this release, DeepSeek is positioning itself as a serious open-source challenger to Gemini 2.5 Pro, and in some cases, it even brushes up against the performance of OpenAI’s o3 and o4-mini on coding benchmarks.
In this blog, we’ll dive into what makes R1-0528 tick, walk through its key new features, and show you how to access it. We’ll also run a hands-on comparison between R1 and R1.1, testing how both models perform on real-world tasks.
Table of Contents
What is DeepSeek R1-0528?
DeepSeek R1 0528 (also referred to as R1.1) is the latest open-source large language model from DeepSeek, designed to push the boundaries of reasoning, code generation, and complex problem-solving. With this release, DeepSeek positions itself as a serious competitor to top-tier proprietary models like those from OpenAI and Google, all while remaining fully open and accessible.
Ideal for researchers, developers, and businesses, R1 0528 offers cutting-edge AI capabilities without locking users into closed ecosystems or costly subscriptions.
Also Read: Everything you need to know about DeepSeek R1
What’s New in DeepSeek R1-0528?
Some of its upgraded features are:
- Massive Parameter Scale: It is trained on a whopping 671 billion parameters. This opens up a lot of possibilities for powerful and efficient processing.
- Fully Open-Source: It is a fully open-source model, allowing for transparency and community-driven upgrade potential.
- Smarter Reasoning: The model achieves improvements in reasoning capabilities, leading to better logic and problem-solving.
- Better Code Generation: It provides better accuracy and efficiency in code generation, coming closer to the leading closed-source models.
- Enhanced Reliability: It is also more reliable and consistent with responses.
- Longer Thinking: The model is capable of thinking for longer durations over complex problems, showcasing a much better performance than its previous version.
How to Access DeepSeek R1-0528?
You can access and use the DeepSeek R1 0528 model in 2 ways: through Hugging Face and through OpenRouter. Here are the instructions to follow:
Via Hugging Face
- Open the DeepSeek R1-0528 model page on Hugging Face.
- Go to the Inference API tab.
- Type your prompt in the provided box.
- Click “Compute” to chat with the model.
To download the model for local use:
- First, scroll to the “Files and versions” section on the model page.
- Then, download the model weights (e.g.,
.bin,.safetensors) and use it with Hugging Face Transformers or Text Generation Inference.
Via OpenRouter
You can directly access the chat interface on OpenRouter (Chat) through this link.
Note: You may need to log in to use the chat interface.
To get the API access for DeepSeek R1 0528,
- First, visit the OpenRouter API Key Page.
- Sign in and get your API key.
- Use the key with any HTTP client or SDK (e.g.,
fetch,axios, or OpenAI-compatible SDKs) to use the model.
DeepSeek R1 0528: Performance Benchmarks
The earlier version of DeepSeek R1 blew the world away with its performance. It gave tough competition to all the popular models at the time and proved that open-source models could be at par with closed-source ones. Now, DeepSeek R1.1 also proves to be quite similar in its impact!
Let’s look at the performance of DeepSeek R1.1 against the top models on the composite LLM performance score, which is aggregated from benchmarks like MMLU, HumanEval, GSM8K, BBH, TruthfulQA, etc.
With a median of 69.45, DeepSeek R1 0528 performs reliably across a wide range of tasks (e.g., reasoning, coding, math, etc.). It delivers near Claude-level median performance at a fraction of the cost, making it one of the best value-for-money models in this list. It outperforms Gemini 2.5 Pro and even Claude Sonnet 4 in consistency while costing 5x–7x less.
Looking at the individual benchmark tests, we can clearly see that the R1 0528 model is a major upgrade from the DeepSeek R1.
DeepSeek R1 0528 showcases exceptional mathematical skills, gaining 2nd place in the AIME 2024 and 2025 benchmark tests, inching very close to OpenAI’s o3. The same is seen in the GPQA Diamond benchmark, LiveCode Bench, and Humanity’s Last Exam, further proving the model’s expertise in general reasoning and coding.
DeepSeek R1 0528 vs DeepSeek R1
Now let’s get to a real-world comparison of DeepSeek R1 and R1 0528 on reasoning, code generation, and reliability. We’ll be testing out both models on 3 different tasks to see how they perform and find out if the new upgrade actually does better.
Task 1: Construct a User Interface Similar to Instagram
Both models will be tasked with building an HTML page that mimics the Instagram design. This will test their code generation quality, structural understanding of UI, and correctness of logic in frontend development.
Input Prompt: “Create a responsive HTML and CSS layout that looks like Instagram’s main feed page. It should include:
A fixed top navigation bar with the Instagram logo on the left, a search bar in the center, and navigation icons (home, messages, explore, notifications, profile) on the right.
A sidebar on the left for navigation with icons and labels similar to Instagram (Home, Search, Explore, Reels, Messages, Notifications, Create, Profile).
A main feed in the center showing several post cards. Each post should include:
- A user avatar and username at the top
- An image
- Action icons (like, comment, share, save)
- A like count
- A caption
- A comment section
A sidebar on the right with suggestions for “People you may know.”
DeepSeek R1 0528 Output:
DeepSeek R1 Output:
Output Comparison
DeepSeek R1 0528 showed smoother rendering, much better UI responsiveness, and somewhat more aligned frame synchronization with each other. The image quality seems slightly sharper, too, probably because of improved internal sampling or export methods.
DeepSeek R1’s transitions and rendering seem a bit slower in comparison. It has minimal frame lags or delays regarding processing, particularly in action-dense areas or transitioning frames.
These observations imply that the R1 0528 update perhaps includes improvements to performance and visual fidelity.
| Feature | DeepSeek R1-0528 | DeepSeek R1 |
| Encoding | Properly encoded | Missing duration metadata |
| Rendering Fluidity | Smooth and responsive | Minor lags on frame transitions |
| Visual Quality | Crisper visuals | Slightly soft |
| Responsiveness | Improved, especially in UI updates | Moderate |
Task 2: Planning a Trip to India
Each model will be tasked with curating a cost-effective trip to India. They will have to include an itinerary, along with costs and rationale. This will test their planning skills, knowledge of the real world, as well as their ability to create structured and actionable content.
Input Prompt: “Plan a 3-day trip to India focused on tech, food, and history with the price of expenses mentioned.“
Output by DeepSeek R1 0528 (truncated):
3-Day India Trip: Delhi & Agra – Tech, Food & History Adventure
(Budget: Mid-Range Solo Traveler) 💰 Total Cost: ~₹25,000–30,000 ($300–360 USD)
Prices cover food, transport, entry fees, and accommodation. Flights not included.
Day 1: Delhi – History & Food
Focus: Mughal/Colonial History + Street Food
Accommodation: Mid-range hotel (Karol Bagh/Aerocity) – ₹2,500/night
- Morning (History):
- Red Fort (UNESCO Site): Explore Mughal architecture. (Entry: ₹500)
- Jama Masjid: India’s largest mosque. (Free entry; ₹300 for tower access)
- Lunch (Food): Karim’s (Old Delhi) – Iconic Mughlai kebabs & biryani. (₹400)
- Afternoon (History):
- Raj Ghat: Gandhi’s memorial. (Free)
- Humayun’s Tomb (UNESCO Site): Pre-Taj Mughal marvel. (Entry: ₹600)
- Dinner (Food): Paranthe Wali Gali – Fried breads with fillings. (₹200)
- Evening: Walk through Chandni Chowk market.
Daily Cost: ₹4,500 (hotel, food, transport, entries)
Output by Deepseek R1 (truncated):
Here’s a well-balanced 3-day trip plan for India covering tech hubs, historical sites, and food experiences, with estimated expenses (in USD and INR for easy reference).
Day 1: Bangalore – The Silicon Valley of India
Theme: Tech + Modern Indian Food
Morning: Tech Exploration
Visit Infosys Campus or Google Office (Outer View)– See India’s IT hub (no entry inside without permission).
Indian Institute of Science (IISc) – Premier research institute (free entry, but check visiting hours).
Visvesvaraya Industrial & Technological Museum – Interactive tech exhibits (Entry: $2 / ₹150).
Cost: ~$5 / ₹400
Afternoon: Food & Culture
Lunch at Mavalli Tiffin Rooms (MTR) – Famous for South Indian meals (~$8 / ₹600).
Commercial Street Shopping – Quick souvenir stop (optional).
Evening: Modern Bangalore
UB City Mall – High-end tech & luxury brands.
Dinner at Toit Brewpub – Craft beer & fusion food (~$15 / ₹1,200).
Total Day 1 Cost: ~$30 / ₹2,500
You can view the complete output of both models here.
Output Comparison
Both models show strong skills in planning and generating structured content. However, there are differences in the style and depth of their travel planning.
DeepSeek R1 0528 plans a more cohesive travel experience using a focused location (Delhi-Agra) to minimize travel overhead. At the same time, it adds richness with experiences like tech-themed food, marble workshops, and interpretations of contemporary and modern history around culinary traditions.
DeepSeek R1 covers a few major cities – Bangalore, Delhi, and Hyderabad, which certainly provides a breadth of exposure. However, it comes at a much larger cost, making it less viable for this particular use case.
| Aspect | DeepSeek R1-0528 | DeepSeek R1 |
| Itinerary Scope | Focuses on Delhi & Agra – compact, well-paced | Covers 3 cities (Bangalore, Delhi, Hyderabad) – broad but rushed |
| Cost Efficiency | ₹25,000–30,000 ($300–360 USD) – optimized travel & dining | ₹40,000–50,000 ($480–640 USD) – due to flights and luxury dining |
| Practicality | Higher – single base stay, short day trip to Agra | Lower – intercity travel is demanding in 3 days |
| Experience Quality | Immersive, themed days with cultural and modern insights | Generic tech/history coverage with less depth |
Task 3: Logical Reasoning Task
Both models will be presented with a logic puzzle in Physics so we can assess their problem-solving skills, deductive reasoning skills, and clarity of explanations.
Input Prompt: “You have 3 light bulbs in a room and 3 switches outside. How do you determine which switch controls which bulb if you can only enter the room once?”
Output by DeepSeek R1 0528:
To determine which switch controls which light bulb with only one entry into the room, follow these steps:
- Label the three switches outside the room as Switch 1, Switch 2, and Switch 3 for reference.
- Turn on Switch 1 and leave it on for about 5 minutes.
- After 5 minutes, turn off Switch 1 and immediately turn on Switch 2.
- Enter the room right after turning on Switch 2 and observe the state of each bulb:
- If a bulb is on, it is controlled by Switch 2.
- If a bulb is off but warm to the touch, it is controlled by Switch 1 (since it was on long enough to heat up but is now off).
- If a bulb is off and cold to the touch, it is controlled by Switch 3 (since it was never turned on).
Why this works:
- By using the time delay with Switch 1, you allow the bulb to heat up. When you turn it off, it remains warm for a short period.
- Turning on Switch 2 immediately before entering ensures that its bulb is on.
- Switch 3 remains off, so its bulb is off and cold.
- This method leverages both the on/off state and the temperature of the bulb as indicators, allowing you to identify all three with a single entry.
Note: This solution assumes the light bulbs are incandescent and generate noticeable heat when on. If the bulbs are LED or another type that does not produce heat, this method may not work, but traditional puzzles assume heat-generating bulbs.
Output by DeepSeek R1:
The Classic Light Bulb Puzzle: Solution
Problem Statement:
You have 3 switches outside a room and 3 bulbs inside the room. You can turn the switches on or off, but you can only enter the room once to inspect the bulbs. How do you determine which switch controls which bulb?
Step-by-Step Solution:
- Label the switches for clarity (e.g., Switch A, Switch B, Switch C).
- Turn on Switch A and leave it on for about 5 minutes.
– This duration allows the bulb controlled by Switch A to heat up (incandescent bulbs warm up noticeably).
- After 5 minutes, turn off Switch A and immediately turn on Switch B.
– Now:
– Switch A: Off (but bulb is warm)
– Switch B: On (bulb is lit)
– Switch C: Off (bulb is cold and off)
- Enter the room to observe the bulbs:
– Bulb that’s ON → Controlled by Switch B (since it’s currently receiving power).
– Bulb that’s OFF but WARM → Controlled by Switch A (was recently on and heated up).
– Bulb that’s OFF and COLD → Controlled by Switch C (was never turned on).
Key Insight:
- Uses heat (from the bulb being on) and light (current state) as indicators.
- Works only with incandescent bulbs (not LEDs or CFLs, which don’t heat up as noticeably).
Summary Table:
| Bulb State | Controlling Switch |
| On | Switch B |
| Off but Warm | Switch A |
| Off and Cold | Switch C |
This method reliably maps all 3 switches to their bulbs in a single room entry.
Output Comparison
Both models accurately identify the puzzle solution using the standard approach of using heat and light to eliminate switches. However:
DeepSeek R1 0528 provides a more concise, readable, and efficient answer, where it highlights practical steps and includes a helpful note about bulb type compatibility.
DeepSeek R1 offers a more instructional, formal organization of text with helpful headings, a summary table, and slightly more elaboration on all of the decision steps.
| Aspect | DeepSeek R1 | DeepSeek R1-0528 |
| Correctness | Correct and complete | Correct and complete |
| Clarity of Explanation | Slightly verbose with a formal tone | More concise and user-friendly |
| Structure & Format | Uses headers, markdown, and a summary table | Bullet-pointed, sequential; ends with a rationale paragraph |
| Extra Insight | Highlights bulb types and scenario assumptions | Adds note about LED vs incandescent compatibility |
Final Verdict
DeepSeek R1-0528 showed significant performance enhancements compared to R1 across all tasks. In trip planning, New Deepseek R1 provided a more useful cost-effective, and organized itinerary. The video output evidenced a smoother and more polished output that likely implies improved rendering. In the logic puzzle activity, both solved the task correctly, however, R1 0528 also gave a more brief and natural description. Overall, R1 0528 is more usable, flexible/ relevant, and grounded for day-to-day tasks.
Conclusion
DeepSeek R1 0528 is a considerable advancement in the democratization of advanced AI technologies. It combines state-of-the-art performance with open-source availability to challenge proprietary models with its compatibility and ease of use, while allowing more individuals to leverage cutting-edge AI for research, development, and business use. Whether you are a developer who wants to build intelligent applications, a researcher eager to explore novel AI frontiers, or a business wanting cost-effective and innovative solution providers, DeepSeek R1 0528 is a uniquely focused and capable platform for the future.
Data Scientist | AWS Certified Solutions Architect | AI & ML Innovator
As a Data Scientist at Analytics Vidhya, I specialize in Machine Learning, Deep Learning, and AI-driven solutions, leveraging NLP, computer vision, and cloud technologies to build scalable applications.
With a B.Tech in Computer Science (Data Science) from VIT and certifications like AWS Certified Solutions Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Fake News Detection, and Emotion Recognition. Passionate about innovation, I strive to develop intelligent systems that shape the future of AI.
