DeepSeek R1 vs OpenAI o1
Two reasoning models with the same basic idea and very different licences: DeepSeek R1 ships open weights you can self-host, while OpenAI o1 is API-only but stronger on the hardest multi-step problems.
| Spec | DeepSeek R1 | OpenAI o1 |
|---|---|---|
| Released | Jan 20, 2025 | Dec 5, 2024 |
| Context window | 128K tokens | 200K tokens |
| Licence | Open weights | Closed / API |
| Inputs | text | text, image |
| Public API | Yes | Yes |
DeepSeek R1
Open-weight reasoning model that popularized reinforcement-learned chain-of-thought at scale.
OpenAI o1
OpenAI o1 is a reasoning model that thinks through a problem internally before replying, trading latency and cost for much higher accuracy on maths, science and difficult programming tasks. It reached general availability in December 2024 and set the template for later reasoning models.
Differences
| Attribute | DeepSeek R1 | OpenAI o1 |
|---|---|---|
| Released | January 2025 | December 2024 |
| Licence | Open weights (MIT) | Closed, API only |
| Context window | 128K tokens | 200K tokens |
| Inputs | Text | Text, image |
| Reasoning trace | Visible | Hidden |
| Self-hosting | Yes | No |
| Relative cost | Low | High |
| Best for | Volume reasoning, on-premise | Hardest correctness-critical tasks |
Verdict
Analysis
The short version
Both models answer by reasoning first and replying second. DeepSeek R1 (January 2025) was the first widely available open-weight model to do this well; OpenAI o1 (December 2024) remains the more polished commercial product.
Reasoning quality
o1 generally holds an edge on competition maths, research-grade physics and long multi-file debugging. R1 closes much of that gap on standard benchmarks and, unusually, exposes its reasoning trace, which is useful when you need to audit how an answer was reached.
Cost and control
R1's open weights mean you can run it on your own hardware, fine-tune it, and keep data in your own environment. o1 is API-only, priced per token, and reasoning tokens are billed but hidden.
Context and inputs
o1 accepts images as part of a reasoning chain and offers a 200K-token context window. R1 is text-only with 128K tokens.
Practical guidance
Use o1 for correctness-critical work where a wrong answer is expensive. Use R1 for high-volume reasoning, on-premise requirements, or research where an inspectable chain of thought matters.
Frequently asked
- Is DeepSeek R1 as good as o1?
- On many public reasoning benchmarks it is close, and it is far cheaper. On the hardest maths, science and large-codebase tasks o1 still tends to be more reliable.
- Can I run either model myself?
- R1 has open weights and can be self-hosted or fine-tuned. o1 is only available through OpenAI's API.
- Which is cheaper?
- R1, substantially — both through third-party hosts and when self-hosted at volume.
- Do both show their reasoning?
- R1 exposes its reasoning trace. o1 keeps it private and bills for it as hidden reasoning tokens.