When DeepSeek R1 launched in January, it quickly became one of the most discussed open-source models, gaining recognition for its sharp reasoning and excellent performance. Fast forward to today, and DeepSeek is back with what's described as a "minor trial upgrade," but don't let the humble label deceive you. DeepSeek-R1-0528 represents substantial progress in reasoning, code generation, and overall reliability. With this release, DeepSeek is positioning itself as a serious contender to Gemini 2.5 Pro in the open-source domain, and in certain areas, it even approaches the performance of OpenAI’s o3 and o4-mini on coding benchmarks.

In this article, we'll explore what makes R1-0528 stand out, review its key new features, and guide you on how to access it. We'll also conduct a hands-on comparison between R1 and R1.1, assessing their performance on real-world tasks.

Table of Contents

• What is DeepSeek R1 0528?
• What’s New in DeepSeek R1 0528?
• How to Access DeepSeek R1 0528?
  • Via Hugging Face
  • Via OpenRouter
• DeepSeek R1 0528: Performance Benchmarks
• DeepSeek R1 0528 vs DeepSeek R1
  • Task 1: Designing an Instagram-like User Interface
  • Task 2: Organizing a Trip to India
  • Task 3: Solving a Logical Reasoning Problem
  • Final Verdict
• Conclusion

What is DeepSeek R1 0528?

DeepSeek R1 0528 (also called R1.1) is the latest open-source large language model from DeepSeek, engineered to push the limits of reasoning, code generation, and complex problem-solving. With this release, DeepSeek aims to establish itself as a strong open-source rival to top-tier proprietary models like those from OpenAI and Google, while maintaining full openness and accessibility.

Perfect for researchers, developers, and businesses, R1 0528 offers cutting-edge AI capabilities without locking users into closed systems or expensive subscription models.

Also Read: All About DeepSeek R1

What’s New in DeepSeek R1 0528?

Among its upgrades are:

• Enormous Parameter Count: It is trained on a staggering 671 billion parameters, opening up numerous possibilities for powerful and efficient processing.
• Completely Open-Source: It is an entirely open-source model, promoting transparency and community-driven enhancements.
• Enhanced Reasoning: The model shows marked improvements in reasoning abilities, leading to better logic and problem-solving.
• Improved Code Generation: It generates code with greater accuracy and efficiency, coming close to the performance of leading closed-source models.
• Increased Reliability: It is also more dependable and consistent in its responses.
• Extended Thinking Time: The model can think for longer periods on complex problems, demonstrating significantly better performance than its predecessor.

How to Access DeepSeek R1 0528?

You can access and utilize the DeepSeek R1 0528 model via two methods: Hugging Face and OpenRouter. Follow these instructions:

Via Hugging Face

1. Open the DeepSeek R1-0528 model page on Hugging Face.
2. Navigate to the Inference API tab.
3. Input your prompt in the provided box.
4. Click “Compute” to interact with the model.

For downloading the model for local use:

1. Scroll down to the “Files and versions” section on the model page.
2. Download the model weights (e.g., .bin, .safetensors) and use them with Hugging Face Transformers or Text Generation Inference.

Via OpenRouter

Access the chat interface on OpenRouter (Chat) directly through this link.

Note: Logging in may be required to use the chat interface.

To obtain API access for DeepSeek R1 0528:

1. Visit the OpenRouter API Key Page.
2. Log in and retrieve your API key.
3. Use the key with any HTTP client or SDK (e.g., fetch, axios, or OpenAI-compatible SDKs) to interact with the model.

DeepSeek R1 0528: Performance Benchmarks

The initial version of DeepSeek R1 stunned the world with its performance, offering stiff competition to all popular models at the time and proving that open-source models could rival closed-source ones. Now, DeepSeek R1.1 continues to make waves!

Let’s examine the performance of DeepSeek R1.1 against top models based on the composite LLM performance score, which aggregates results from benchmarks like MMLU, HumanEval, GSM8K, BBH, TruthfulQA, etc.

With a median of 69.45, DeepSeek R1 0528 performs consistently 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 surpasses Gemini 2.5 Pro and even Claude Sonnet 4 in consistency while costing 5x–7x less.

Looking at individual benchmark tests, it’s clear that the R1 0528 model is a major leap forward from DeepSeek R1.

DeepSeek R1 0528 demonstrates exceptional mathematical prowess, securing 2nd place in the AIME 2024 and 2025 benchmark tests, closely matching OpenAI’s o3. The same holds true in the GPQA Diamond benchmark, LiveCode Bench, and Humanity’s Last Exam, further highlighting the model’s expertise in general reasoning and coding.

DeepSeek R1 0528 vs DeepSeek R1

Now, let’s compare DeepSeek R1 and R1 0528 in real-world scenarios focusing on reasoning, code generation, and reliability. We’ll test both models on three distinct tasks to evaluate their performance and determine if the upgrade truly brings improvements.

Task 1: Designing an Instagram-like User Interface

Both models will be tasked with creating an HTML page resembling Instagram’s main feed. This will test their code generation quality, understanding of UI structure, and logical correctness in frontend development.

Input Prompt: “Create a responsive HTML and CSS layout that resembles 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 displaying 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 0528 demonstrated smoother rendering, much better UI responsiveness, and slightly crisper visuals, possibly due to enhanced internal sampling or export techniques.

DeepSeek R1’s transitions and rendering appeared slower in comparison. There were minimal frame lags or delays, especially during action-heavy scenes or transitions.

These observations suggest that the R1 0528 update might include performance and visual fidelity improvements.

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