The newly released DeepSeek R10528 is a significant upgrade to the original DeepSeek model, boasting impressive capabilities. This massive 671 billion parameter model, with 37 billion active parameters for inference, leverages a sparse mixture of experts architecture to deliver high performance efficiently.

Despite the lack of an official announcement, the model has been quietly released through the DeepSeek AI Hugging Face profile, available under the MIT license. Initial reports suggest that this upgraded model has significantly improved the reasoning capabilities of the original DeepSeek, making it more suitable for real-world use cases.

The benchmark results are particularly noteworthy, with the DeepSeek R10528 model performing close to the performance of the O4 Mini and G3 High models on the Massive Multitask Language Understanding (MMLU) benchmark. This is an impressive feat, considering that the DeepSeek R10528 is an open-source model competing against proprietary models.

The model's strong performance in hard question accuracy also showcases its competitive general reasoning ability, which is particularly impressive given its open-source nature. This development is exciting, as it demonstrates the continued progress in the field of AI reasoning models, with the DeepSeek R10528 emerging as a powerful and efficient alternative to the more well-known AI assistants.

The new DeepSeek R10528 model has showcased impressive benchmark results, rivaling the performance of established models like GPT-3 Mini and GPT-3 High. On the Massive Multitask Language Understanding (MMLU) benchmark, the DeepSeek R10528 model has achieved scores that are close to the performance of the GPT-4 Mini model, demonstrating its strong general reasoning capabilities.

Notably, the DeepSeek R10528 model is an open-source model, going up against proprietary models from major AI companies. This makes its competitive performance even more impressive, as it highlights the advancements in open-source AI technology.

In terms of hard question accuracy, the DeepSeek R10528 model has also performed well, showcasing its ability to handle challenging reasoning tasks. This suggests that the model has made significant improvements in its long-term reasoning and problem-solving skills compared to the previous DeepSeek model.

Overall, the benchmark results for the DeepSeek R10528 model are very promising, indicating that this new upgraded version of the DeepSeek model is a powerful and capable reasoning tool that can compete with some of the top AI models in the industry.

You can access the DeepSeek R1 model through the DeepSeek API or through the Open Router's free API. The DeepSeek API provides a paid version with no rate limits.

The context for the DeepSeek R1 model is set at approximately 136K, and it is listed at $195 for 1 million input tokens and $5 for 1 million output tokens, which is reasonably priced for a reasoning model.

To test the capabilities of the DeepSeek R1 model, you can try various prompts, including reasoning, coding, and mathematics tasks. The model has demonstrated strong performance in chain-of-thought math reasoning, generating creative visuals, and common sense reasoning with counterfactual thinking.

Overall, the DeepSeek R1 model appears to be a significant upgrade from the previous version, showcasing improved reasoning abilities and performance compared to other established models. As the team continues to work on the upcoming DeepSeek R2 model, this release is a promising step forward in the development of powerful open-source reasoning models.

Since we don't have a lot of information on this new DeepSeek R10528 model, let's put it to the test with a variety of prompts to assess its capabilities.

First, we'll tackle a chain of thought math reasoning problem. We'll ask the model: "A train travels at 60 mph for 45 minutes, then 30 mph for 30 minutes. What is the total distance it traveled, and what is the average speed over the entire trip?" This will test the model's ability to perform multi-step arithmetic reasoning, handle unit conversions, and maintain numerical consistency.

Next, we'll challenge the model's creativity and coding skills. We'll ask it to "draw a beautiful sunset skybox that would be perfectly at home in an early 2000s Sega video game." We'll then request it to "create a SaaS landing page with as many features as possible" to evaluate its front-end design capabilities.

Finally, we'll assess the model's common sense reasoning and hypothetical thinking. We'll pose the following prompt: "It's raining, and John didn't take his umbrella. What is the most likely outcome, and what could John have done differently to avoid this outcome?" This will test the model's ability to simulate real-world scenarios and provide meaningful, context-aware responses.

Let's see how the DeepSeek R10528 model performs on these diverse tasks and see if it can live up to the initial reports of its impressive capabilities.

In conclusion, the new DeepSeek R10528 model is a significant upgrade from the previous DeepSeek model. With a massive 671 billion total parameters and 37 billion active parameters, this model leverages a sparse mixture of experts architecture to deliver high performance efficiently.

The benchmark results are impressive, with the model performing close to the GPT-4 Mini and GPT-3 High on the MMLU (Massive Multitask Language Understanding) benchmark. This showcases the model's competitive general reasoning ability, especially considering that it is an open-source model going up against proprietary models.

The model's performance across various prompts, including chain of thought math reasoning, coding and creative tasks, as well as common sense and counterfactual reasoning, demonstrates its versatility and capability. The model was able to provide accurate and well-reasoned responses, highlighting its potential for real-world use cases.

Overall, the release of the DeepSeek R10528 model is a significant step forward for the DeepSeek team, and it will be exciting to see the further advancements in the upcoming DeepSeek R2 model. This model's open-source nature and impressive performance make it a compelling option for developers and researchers alike.