Build anything with DeepSeek R1, here’s how

DeepSeek R1 is positioned as an open-source reasoning model that matches OpenAI’s o1-level performance while being dramatically cheaper—about 27x lower token costs—arriving only about 46 days after o1 launched. The pitch hinges on two practical advantages: cost and transparency. DeepSeek’s pricing puts input at $0.55 per million tokens and output at $2.2 per million tokens, compared with o1’s $15 (input) and $60 (output) per million tokens. That price gap matters because it makes “reasoning-heavy” applications feasible for individuals and small teams, not just large labs.

Beyond benchmarks, the transcript emphasizes a custom evaluation and a qualitative difference in how reasoning can be inspected. A Twitter user reportedly built their own eval where DeepSeek R1 “destroyed” other models, and the model’s reasoning is shown via a visible chain-of-thought-style output. The creator contrasts this with OpenAI’s o1/o1-preview experience, where reasoning tokens are paid for but not shown to users. The ability to see the model “think through a problem,” including mistakes, is presented as a major usability and debugging upgrade.

The transcript then traces how DeepSeek achieved this capability: less time spent on safety compared with OpenAI’s longer safety cycle, plus a training approach labeled “R1-Z,” meaning it starts from “zero” supervised training data and relies on reinforcement learning. The analogy is AlphaZero’s reinforcement learning success in mastering Go, and the key claim is that reinforcement learning produced an emergent behavior—longer thinking time leading to better outcomes without explicit instruction. The model is also described as part of a broader release strategy: DeepSeek reportedly dropped six smaller distilled models ranging from 70B down to 1.5B, with the smallest potentially runnable on a phone.

Finally, the transcript shifts from claims to implementation, walking through how to build an app using DeepSeek’s platform and API. Steps include creating a DeepSeek account, topping up a small amount (the transcript suggests $2 is enough given low token costs), generating an API key, and using Python with the OpenAI-compatible client library. The example prompt asks for high-leverage actions to prepare for a post-AGI world, then sets the model to “DeepSeek Reasoner” (to avoid defaulting to a less capable DeepSeek V3). The walkthrough also adds token streaming so reasoning output appears live in the console.

To turn a single response into a “team of agents,” the transcript uses multi-round conversation support from DeepSeek’s documentation and then implements a second agent in Cursor that takes the first agent’s “content” and asks follow-up questions—producing a more structured day-to-day plan. The result is a practical schedule (daily and weekly activities) for building AI literacy and resilience.

Overall, the core message is that DeepSeek R1’s combination of open availability, visible reasoning, and steeply lower inference costs makes it realistic for solo developers to compete with larger companies—especially by embedding it into agentic workflows and productivity tools.