DeepSeek’s R1 is making waves, but is it truly a game-changer? In this blog, we clear the smoke, evaluating R1’s real impact, efficiency gains, and limitations. We also explore how organizations should think about R1 as they look to leverage AI responsibly.
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Randall Hunt, Chief Technology Officer at Caylent, is a technology leader, investor, and hands-on-keyboard coder based in Los Angeles, CA. Previously, Randall led software and developer relations teams at Facebook, SpaceX, AWS, MongoDB, and NASA. Randall spends most of his time listening to customers, building demos, writing blog posts, and mentoring junior engineers. Python and C++ are his favorite programming languages, but he begrudgingly admits that Javascript rules the world. Outside of work, Randall loves to read science fiction, advise startups, travel, and ski.
View Randall's articlesAmid the buzz surrounding DeepSeek's launch of R1, the reaction has ranged from excitement to overhyped panic. Let's cut through the noise. While DeepSeek's achievements with R1 are significant, the broader narrative should focus on their methodology and the implications for the AI landscape rather than the exaggerated claims of market disruption.
The headlines claiming R1 has unseated industry titans like Claude are overstated. DeepSeek has indeed made impressive strides, leveraging synthetic training data to fine-tune a smaller model that converges faster on meaningful results. If anything, this is a showcase of their ability to creatively repurpose the computational investment made in earlier, larger models by the industry’s AI juggernauts. These advances come from the way the training data is introduced, not from groundbreaking changes to model architecture or topology.
This is not a revelation to those familiar with AI development. Techniques like model distillation—compressing knowledge from larger models into smaller, efficient ones—have long been celebrated in the research community. What R1 does exceptionally well is demonstrate how distillation can expand not just task-specific performance but also broader capabilities.
Efficiency gains like this are good for everyone. Smaller, more efficient models mean reduced computational costs and expanded accessibility, empowering a broader range of applications. Amazon Bedrock, for example, already supports model distillation, and Caylent actively employs similar techniques to deliver cost-effective, high-performing solutions for customers.
At Caylent, we focus on platform-agnostic solutions, helping customers select and deploy the best models for their unique needs. DeepSeek R1 aligns with this philosophy by offering flexibility. The open-source nature of its weights, licensed under MIT, allows organizations to run the model securely and privately on their preferred hardware or cloud platforms. For instance, users can deploy DeepSeek R1 within Amazon Bedrock using the Custom Model Import feature or operate it on SageMaker across a few GPUs. These options ensure full control over deployments, eliminate concerns about data sharing, and provide a safe, reliable way to harness the model's capabilities.
While R1 is a step forward, it's not without limitations. Our evaluation reveals that R1 struggles with some proprietary tests where Claude, Nova, and similar models excel. This could point to overfitting to specific benchmarks—a common issue in early-stage models. Additionally, while R1's per-token cost is lower, it uses more tokens per query, which could inflate costs in certain edge cases.
From Caylent’s vantage point, customers consistently prioritize "the highest quality tokens, as quickly as possible, at the cheapest price." This underscores the importance of evaluating model performance in specific business contexts rather than relying solely on industry benchmarks. Our initial review of R1 shows impressive performance in areas like quantitative reasoning but highlights challenges with coding tasks, single-language support, speed, and latency.
Another point of contention is the hosted API and app offering from DeepSeek. Data routed through these first-party solutions may be visible to state-level actors, raising legitimate privacy concerns. The solution? Host the model yourself for maximum security and control.
One of the major concerns surrounding DeepSeek is whether using its models compromises user data. If you rely on DeepSeek’s first-party hosted API or app, it is likely that traffic could be monitored and shared with state-level actors. However, the models and weights are open source under a permissive MIT license. This means anyone can run the models independently, ensuring they remain private and secure. For example, DeepSeek R1 can be deployed on Amazon Bedrock or SageMaker using their Custom Model Import feature, allowing organizations to avoid data privacy concerns while taking full advantage of the model’s capabilities. Additionally, many follow-up projects leveraging DeepSeek’s distillation techniques have enabled other open-source models, like Meta’s Llama family, to incorporate these improvements without any risk of data exposure.
It’s also worth noting that reward modeling (i.e. the way that the model is influenced to respond based on human feedback after the initial training) in R1 appears to be influenced by Chinese censorship laws. For instance, the model refuses to answer questions about sensitive topics like Tiananmen Square. These nuances highlight the importance of understanding how a model’s training data and governance might impact its performance and ethical use.
DeepSeek’s next move, with the upcoming Janus-Pro 7B—a multimodal model with chain-of-thought reasoning capabilities—has the potential to further disrupt the landscape. But for now, DeepSeek hasn’t dethroned anyone. The AI race continues, with R1 proving to be a strong contender but not a decisive champion.
For organizations, this reinforces that we are in store for exciting opportunities to optimize workflows with increasingly efficient models. For the industry, it underscores the importance of cutting through the hype and relying on expert analysis.
The lesson here? Celebrate progress, embrace efficiency gains, but don’t let sensationalism overshadow measured, thoughtful evaluation. Most importantly, design your AI solutions to replace the core LLMs regularly. The future of AI is as much about collaboration and iteration as it is about breakthroughs.
At Caylent, our AI experts have helped hundreds of customers deploy impactful AI solutions that are rapidly reshaping industries, like BrainBox AI and their incredible contributions to global energy sustainability. We follow a production focussed approach that maximizes your ability to extract ROI from your deployments. If you’d like to explore how generative AI can add value to your business, get in touch with our experts!
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Randall Hunt