By Jim Shimabukuro (assisted by ChatGPT)
Editor
As of November 2, 2025, DeepSeek’s R1 model stands as one of the most consequential open-source achievements in artificial intelligence. Released earlier in the year, R1 captured global attention for its advanced reasoning capabilities and its daringly open release strategy. It has since become a cornerstone in the conversation about how the next generation of AI should be trained, shared, and governed.
When R1 first appeared in January 2025, it was billed as a large-scale reasoning model that used reinforcement learning (RL) as its primary training method, rather than relying primarily on the supervised fine-tuning approach used by most commercial models. This change proved revolutionary. Instead of being “taught” the right answers from massive text corpora, R1 learned to improve its reasoning ability through trial and reward, honing its step-by-step logic on math, coding, and problem-solving tasks. The result was a model that could outperform—or at least match—major Western models on complex reasoning benchmarks, despite being trained at a fraction of their cost.
From the beginning, DeepSeek’s decision to open-source R1 under a permissive license set it apart from competitors. The full-sized 671-billion-parameter model, as well as its smaller distilled versions, were made available through public repositories such as Hugging Face. This openness not only invited collaboration from researchers around the world but also created an alternative to the increasingly closed ecosystems of models like OpenAI’s GPT-4 or Anthropic’s Claude series. For the broader AI community, R1 symbolized the return of accessibility and transparency to frontier-level research—qualities that had been fading from the industry’s top tiers.
DeepSeek continued to refine R1 through a series of updates that kept the model relevant throughout 2025. A major revision, R1-0528, appeared in May 2025, improving the system’s performance on key logic and programming benchmarks while adding new features such as structured JSON output and built-in function-calling. These upgrades made R1 more usable in practical applications, from code generation to workflow automation. The R1 family also benefited from DeepSeek’s innovative “distillation” process, in which the capabilities of the large model were compressed into smaller, more efficient versions. This technique allowed R1’s core reasoning strength to reach a broader audience, including developers and institutions without access to massive compute clusters.
While R1 remains widely used and studied, DeepSeek has not stood still. Throughout 2025 the company shifted much of its attention to newer models in its evolving architecture, most notably the V3.1 Terminus and V3.2 Exp releases. These successors, launched in September 2025, build upon the foundations of R1 while integrating new methods for context handling and safety alignment. The official DeepSeek API logs show that R1 remains supported and fully operational, but the company now presents the V3.x line as its cutting-edge offering. In effect, R1 has moved from being the “frontier” to becoming the “benchmark”—still powerful and accessible, but no longer the centerpiece of the company’s research roadmap.
This transition has not diminished R1’s significance. The model continues to be hosted on platforms like Azure AI and GitHub, demonstrating that it has moved beyond the laboratory into commercial deployment. Microsoft, among others, has recognized its stability and cost-effectiveness, making it available to enterprise developers as an open-source reasoning engine. For research and education, R1’s open nature has been transformative. Universities and independent labs can now run experiments that would previously have been impossible without proprietary access or multimillion-dollar compute budgets.
However, the model’s openness has also brought scrutiny. Several independent studies have raised questions about R1’s alignment and safety behaviors. One 2025 paper reported a 100 percent “attack success rate” when the model was exposed to certain adversarial prompts in Chinese-language safety tests, indicating vulnerabilities to harmful or restricted content generation. Another study, nicknamed “R1dacted,” explored the model’s tendency to refuse or filter politically sensitive responses, revealing inconsistencies between different language or regional deployments. These findings have fueled ongoing debates about how to balance openness with responsible release practices, particularly for models capable of high-level reasoning and complex planning.
From a technical perspective, R1 also faces challenges familiar to large-scale models. Its token consumption is immense, meaning that solving complex multi-step problems often produces extremely long chains of reasoning—sometimes thousands of tokens in a single query. While this is partly what gives R1 its depth of analysis, it also makes the model more expensive to run at scale. DeepSeek and the broader open-source community have responded by working on efficient inference methods, pruning techniques, and further distilled variants to make R1’s reasoning capabilities more computationally practical.
Economically, the presence of R1 has already altered the competitive landscape. Its release forced other AI companies to reconsider their pricing and licensing strategies, leading to what some analysts have called an “AI price war.” Because R1 demonstrated that high-quality reasoning could be achieved without the multi-billion-dollar infrastructure of Western labs, it challenged long-held assumptions about the relationship between investment scale and model capability. The open-source ecosystem has benefited immensely: new derivative models have been trained using R1 checkpoints, and research citing R1 now spans fields from education to automated theorem proving.
DeepSeek’s CEO and founder, Liang Wenfeng, continues to lead this expansion. With roots in quantitative finance and data-driven trading through his company High-Flyer, Liang has applied the same resource-intensive but strategically disciplined philosophy to AI development. His team—led by core researchers such as D. Guo, Aixin Liu, Bei Feng, Bing Xue, and Bingxuan Wang—has grown into one of the most productive independent AI research groups in Asia. Together, they’ve demonstrated that world-class AI innovation can thrive outside of Silicon Valley’s traditional ecosystem, signaling a more multipolar future for global AI leadership.
In summary, DeepSeek R1 remains fully live, stable, and open as of November 2, 2025, with a vibrant developer and research community still building upon it. Yet within DeepSeek’s own roadmap, R1 now plays the role of a proven foundation rather than a frontier experiment. The company’s newer V3 models are beginning to define the state of the art, incorporating lessons learned from R1’s triumphs and shortcomings alike.
Still, R1’s legacy is secure. It redefined what an open reasoning model could achieve, proving that deep logic and transparency need not be mutually exclusive. Whether viewed as a scientific milestone, a commercial disruptor, or a philosophical statement about the democratization of intelligence, R1’s influence will continue to echo throughout the AI world for years to come.
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Educational Technology and Change Journal 
[…] the overlaid restrictions that cause issues. This has sparked debates in educational circles, with Educational Technology and Change Journal noting in November 2025 that R1 represents a milestone in open-source AI, yet its flaws underscore […]