The latest update to Deepseek R1 has significantly improved its depth of reasoning and inference capabilities. By leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training, Deepseek R1 has achieved outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic.

Notably, Deepseek R1's overall performance is now approaching that of leading models such as GPT-3 and Gemini 2.5. This is a remarkable achievement, considering that Deepseek R1 is a completely free and open-source model, directly competing with closed-source frontier models developed by leading US tech companies.

The benchmarks showcase Deepseek R1's significant improvements. On the AMY 2024 benchmark, the model's score increased from 79.8 to 91.4, and on AMY 2025, it went from 70 to 87. Similar improvements were observed across other benchmarks, such as GPQA Diamond, Live Codebench, ADER, and Humanity's Last Exam.

When compared to GPT-3, Deepseek R1 is now quite close in performance, with some benchmarks showing nearly identical scores. Surprisingly, Gemini 2.5 Pro, which was previously considered the best coding model, falls behind Deepseek R1 and GPT-3 on most of the benchmarks.

According to the independent analysis by Artificial Analysis, Deepseek R1 has leaped over XAI, Meta, and Anthropic, tying as the world's number two AI lab and the undisputed open-source weights leader. This substantial improvement is comparable to the leap seen from OpenAI's GPT-1 to GPT-3.

Interestingly, the new version of Deepseek R1 uses significantly more tokens to complete the evaluations, indicating that it thinks for longer and more deeply than the previous version. This increased computational effort has paid off in the form of enhanced capabilities.

The narrowing gap between open-source and closed-source models is a significant development in the AI landscape. Deepseek R1's impressive upgrades demonstrate that open-source models can now compete with the leading frontier models, providing a promising future for accessible and transparent AI technology.

The latest update to Deepseek R1 has significantly improved its depth of reasoning and inference capabilities. By leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training, Deepseek R1 has achieved outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic.

Deepseek R1's overall performance is now approaching that of leading models such as GPT-3 and Gemini 2.5. Compared to the previous version, the new Deepseek R1 has seen substantial improvements across multiple benchmarks:

• AMY 2024: 79.8 to 91.4
• AMY 2025: 70 to 87
• GPQA Diamond: 71 to 81
• Live Codebench: 63 to 73
• ADER: 57 to 71
• Humanity's Last Exam: 8.5 to 17.7

When compared to GPT-3, Deepseek R1 is on par or slightly behind in some benchmarks, but it outperforms the industry-leading Gemini 2.5 Pro model across the board.

Deepseek's R1 leap has also been recognized by the Artificial Analysis Intelligence Index, where it has jumped from 60 to 68, matching the same type of increase seen from OpenAI's GPT-1 to GPT-3. This places Deepseek as the world's number two AI lab, tied with the leading closed-source models, and the undisputed open-source weights leader.

The gap between open-source and closed-source models continues to shrink, with Deepseek R1 now comparable to the frontier models from leading tech companies. This release further solidifies Deepseek's position as a highly capable and efficient open-source alternative to the industry's top-performing models.

The latest update to Deepseek R1 has significantly improved its depth of reasoning and inference capabilities. By leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training, Deepseek R1 has achieved outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic.

Deepseek R1's overall performance is now approaching that of leading models such as GPT-3 and Gemini 2.5. Compared to the previous version, the new Deepseek R1 has seen substantial improvements across multiple benchmarks:

• AMY 2024: 79.8 to 91.4
• AMY 2025: 70 to 87
• GPQA Diamond: 71 to 81
• Live CodeBench: 63 to 73
• ADER: 57 to 71
• Humanity's Last Exam: 8.5 to 17.7

When compared to GPT-3, Deepseek R1 is on par or slightly behind in some benchmarks, but it surpasses the leading coding model, Gemini 2.5 Pro, in most evaluations. According to independent analysis by Artificial Analysis, Deepseek R1 has leaped over XAI, Meta, and Anthropic to be tied as the world's number two AI lab, with the undisputed open-source model leadership.

The new Deepseek R1 is a 671 billion parameter model with 37 billion active parameters, and it has seen a significant improvement in its coding skills, now matching Gemini 2.5 Pro in the Artificial Analysis coding index, and only behind GPT-4 Mini and GPT-3.

Deepseek R1's latest update has significantly improved its depth of reasoning and inference capabilities, making it a highly capable open-source model that is now approaching the performance of leading closed-source models like GPT-3 and Gemini 2.5. This is a remarkable achievement, as Deepseek R1 is a completely free and open-source model that directly competes with the frontier models developed by major tech companies.

The benchmarks show that Deepseek R1 has made substantial gains across various evaluations, including mathematics, programming, and general logic. It has now surpassed the performance of models from leading AI labs like Anthropic and Meta, and is tied with OpenAI's GPT-3 on several metrics. Notably, Deepseek R1 is also matching the coding skills of the highly regarded Gemini 2.5 Pro model.

This advancement is significant because it demonstrates the narrowing gap between open-source and closed-source AI models. The ability of Deepseek R1 to achieve such impressive results without the backing of a major tech company highlights the potential of open-source AI development. It also suggests that the global landscape of AI capabilities is becoming more balanced, with China-based AI labs now neck-and-neck with their US counterparts.

The improvements in Deepseek R1 were achieved through continued refinement of its reinforcement learning techniques, allowing the model to extract more intelligence from its original pre-training. This highlights the importance of ongoing model optimization and the potential for open-source models to keep pace with their closed-source counterparts.

Overall, the advancements in Deepseek R1 are a significant milestone for the open-source AI community, showcasing the ability of collaborative, transparent development to produce highly capable models that can compete with the industry's leading closed-source offerings.

While the benchmarks show a significant improvement in Deepseek R1's capabilities, the model's performance on the Rubik's Cube challenge was somewhat disappointing. The model was able to generate a substantial amount of code, demonstrating its improved coding skills, but it ultimately failed to produce a fully functional Rubik's Cube simulation.

The model's thought process, as shown in the transcript, indicates that it was iterating over different approaches to solve the problem. However, the final output did not include a working cube, and the rotation and physics of the cube were not properly implemented.

In comparison, the previous version of Gemini 2.5 Pro was able to complete the task more effectively, showcasing its strong capabilities in this domain. The new Deepseek R1 model, while impressive in its overall performance, still has room for improvement when it comes to executing complex, interactive tasks like the Rubik's Cube simulation.

The advanced snake game test also highlighted some issues, as the model's output resulted in a name error that could not be easily fixed. This suggests that while the model has made significant strides, there are still areas where its execution and problem-solving abilities could be further refined.

Overall, the Deepseek R1 update is a promising step forward, but the model's performance on these specific challenges indicates that there is still work to be done to fully bridge the gap with leading closed-source models in terms of practical, real-world application.

While the benchmarks suggest a significant improvement in Deepseek R1's capabilities, the model's performance on the advanced snake game implementation leaves room for improvement. The generated code, though extensive at 1,117 lines, failed to execute properly, indicating issues with the model's ability to handle more complex programming tasks.

The inability to properly define the player_snake variable and the instant termination of the program suggest that Deepseek R1 still struggles with certain aspects of software development, despite its impressive performance on other benchmarks. This highlights the need for continued refinement and testing to ensure the model's capabilities are well-rounded and can be reliably applied to real-world programming challenges.

Overall, the new Deepseek R1 release represents a significant step forward in open-source AI capabilities, but there is still work to be done to bridge the gap with leading closed-source models, particularly in the realm of complex programming tasks. As the competition between open-source and closed-source AI continues to evolve, it will be crucial for Deepseek to address these areas of weakness to maintain its position as a formidable contender in the AI landscape.

The latest update to Deepseek R1 has significantly improved its depth of reasoning and inference capabilities. The model's performance is now approaching that of leading models like GPT-3 and Gemini 2.5, making it a strong contender in the open-source AI landscape.

The benchmarks show impressive gains across various tasks, including mathematics, programming, and general logic. Deepseek R1 has now surpassed the performance of models from leading US tech companies, cementing its position as a top-tier open-source AI system.

However, the author's own testing of the model's abilities, such as the Rubik's Cube and advanced Snake game tasks, suggests that there is still room for improvement in certain areas. While the model's overall performance is impressive, the author notes that it did not fully meet their expectations in these specific tests.

Nonetheless, the significant progress made by Deepseek R1 is a testament to the continued advancements in open-source AI development. The narrowing gap between open-source and closed-source models is an encouraging trend, and the author remains bullish on the future of Deepseek and other open-source AI agents.