Quick Verdict
DeepSeek R1 was one of the first reasoning models to show its work in full: instead of hiding its chain-of-thought like OpenAI's o1 does, R1 exposes the entire reasoning trace before the final answer. That transparency, combined with an MIT-style open license and benchmark scores that matched or beat o1-mini on math competitions like AIME, is why it made such a large splash on release. It's still a strong, genuinely free option for math- and logic-heavy tasks, though the visible reasoning tokens mean longer, more expensive outputs than a model that reasons silently.
Pros
- ✅ Shows its full chain-of-thought reasoning, not a hidden summary
- ✅ Matched or beat OpenAI's o1-mini on AIME and MATH benchmarks at release
- ✅ Open-weight, MIT-licensed, free to self-host
Cons
- ❌ Visible reasoning traces increase total token output and cost
- ❌ The hosted web chat can slow down noticeably during peak hours
What Is DeepSeek R1?
DeepSeek R1 is a reasoning model trained with reinforcement learning to work through math, logic, and coding problems step by step before answering. Unlike OpenAI's o1 family, which summarizes or hides its internal reasoning, R1 shows the full chain of thought in the response, which makes it easier to audit why the model reached a given answer, and easier to catch when its reasoning goes wrong.
Its open, MIT-style license means the weights can be downloaded and run on your own infrastructure with no licensing fee, which was a significant part of why it drew so much attention on release: a genuinely open model performing in the same range as a closed frontier reasoning model.
Under the hood
R1's training process leaned heavily on reinforcement learning applied directly to reasoning tasks, rewarding the model for reaching correct answers through verifiable steps, math problems with checkable solutions, coding problems that either compile and pass tests or don't, rather than relying purely on human preference ratings. That's a different recipe than how most chat-tuned models are built, and it's part of why R1's reasoning traces look the way they do: long, sometimes meandering chains where the model visibly reconsiders a wrong turn before arriving at an answer.
DeepSeek also released a set of smaller distilled models trained on R1's reasoning outputs, compressing much of that reasoning capability into far smaller parameter counts that run on modest hardware. Those distilled versions trade some raw capability for accessibility, and they're worth considering if the full-size R1 is out of reach for your infrastructure.
Where it falls short
The most-discussed issue with R1 has nothing to do with reasoning quality: it's where the data goes. The hosted web chat and API route requests through DeepSeek's infrastructure, based in China, and the company's data handling and content moderation policies have drawn real scrutiny from security researchers and, in some cases, outright bans from government and corporate networks. If you're self-hosting the open weights, this concern doesn't apply, but most casual users interacting through the web chat or API are sending queries to servers outside their control.
The model also applies content moderation aligned with Chinese regulations, which shows up as evasive or refused answers on a narrow set of politically sensitive topics. Most users won't hit this in day-to-day technical work, but it's worth knowing before building a product around it for a general audience. On the output side, the long reasoning traces can also make responses feel sluggish for simple questions where a shorter model would have answered in a fraction of the time and token count.
Pricing
| Option | Cost |
|---|---|
| Self-hosted (open weights) | Free, requires substantial GPU hardware |
| DeepSeek API | Priced per token, well below OpenAI's o1 pricing |
| Web chat | Free with usage limits |
Who Should Use It?
R1 is well suited to math, logic, and coding problems where seeing the reasoning matters, whether that's for debugging the model's own mistakes or for educational use where the steps are as valuable as the answer. Students and educators exploring how a model arrives at an answer, not just what the answer is, get real value from the visible chain-of-thought that a hidden-reasoning model like o1 doesn't expose.
Researchers and hobbyists who want to self-host a genuinely capable reasoning model without a licensing fee are well served by the open weights, and the distilled smaller versions widen that to people without data-center-class hardware. Enterprises with strict data governance requirements should self-host rather than route sensitive queries through the hosted API, given the infrastructure and content-moderation concerns above. Because it outputs full reasoning traces, expect longer responses and higher token costs on the API than a model that reasons silently.
Frequently Asked Questions
Why does DeepSeek R1 show its reasoning while OpenAI's models don't?
It's a design choice. DeepSeek made the full chain-of-thought visible, while OpenAI summarizes or hides it. Visible reasoning is easier to audit but costs more tokens per response.
Is DeepSeek R1 free to use?
The web chat interface is free with usage limits, the weights are free to self-host given the right hardware, and the hosted API is priced per token well below comparable closed models.
Does the visible reasoning make it slower?
Yes, generating the full reasoning trace before the final answer takes longer than a model that reasons silently and returns just the summary, which shows up as both added latency and added cost.
Is it safe to send sensitive company data to DeepSeek R1's hosted API?
We'd recommend caution. The hosted API and web chat route through DeepSeek's China-based infrastructure, which has led some organizations to restrict or ban its use for sensitive data. Self-hosting the open weights avoids this entirely.
What are the distilled versions of R1?
DeepSeek released smaller models trained on R1's reasoning outputs, compressing much of its reasoning capability into parameter counts small enough to run on a single consumer GPU, with some capability tradeoff compared to the full model.
Final Verdict
DeepSeek R1 proved that open, transparent reasoning models could compete with closed frontier options on hard math and logic benchmarks. If you value being able to see and audit a model's reasoning, and can tolerate the token cost that comes with it, it remains one of the more interesting options in this category.