A reasoning-focused large language model developed by China’s DeepSeek AI. When it launched in January 2025, it drew worldwide attention for matching OpenAI o1’s math and coding performance at a dramatically lower cost. As a “reasoning model,” it unfolds a long chain of thought before giving its answer, making it strong at tasks that require careful thinking — complex math problems, logic puzzles, and code debugging. Its biggest distinction is that the model weights are released under the MIT License: anyone can download, modify, and use them commercially for free. The May 2025 revision, R1-0528, further deepened its reasoning, significantly improving its accuracy on AIME (comparable to math olympiad qualifiers).
Key Features
- A reasoning model that shows its thinking: Designed to unfold a long thought process before answering. Delivers high performance on tasks that need step-by-step reasoning, such as math, logic, and coding
- Open weights under the MIT License: The model weights are published on Hugging Face and free to modify, redistribute, and use commercially. Self-hosting on your own servers or cloud is also possible
- Excellent cost efficiency: Offering reasoning performance on par with closed models at a fraction of the cost made headlines at launch and pushed price competition across the AI industry
- Distilled versions for lighter environments: Distilled models that transfer R1’s reasoning ability into smaller models (Qwen and Llama based, 1.5B–70B) are also published and can run on a local PC
- 128K-token long context: The R1-0528 revision supports a 128K-token context, handling long documents and large codebases
- JSON output and function calling: R1-0528 added support for JSON output and function calling, along with reduced hallucination rates
Pricing
| Usage | Price | Notes |
|---|---|---|
| Chat app (Web/mobile) | Free | R1-series reasoning available via DeepThink mode |
| Open weights (self-hosted) | Free (MIT License) | Download weights from Hugging Face; infrastructure costs are your own |
| Official DeepSeek API | Varies by model | The dedicated R1 endpoint (deepseek-reasoner) was retired in July 2026; reasoning is now integrated into the successor V4 models |
| Third-party APIs (OpenRouter, etc.) | Varies by provider | Multiple providers continue to host R1 / R1-0528 |
Pricing is as of August 2026. Please check the official site for the latest pricing.
Pros & Cons
✅ Pros
- Weights are published under the MIT License — free to modify, use commercially, and self-host
- Performance rivaling top closed models on math, coding, and logical reasoning benchmarks
- Visible thought process makes it easy to verify the reasoning behind answers, which also suits learning
- Distilled versions let you run a reasoning model locally or on a single GPU
- Free to try instantly via the chat app
⚠️ Cons
- Reasoning takes time, so it is a poor fit for everyday questions that need instant answers
- Self-hosting the full-size model (671B parameters, 37B active) requires a large-scale GPU setup
- As a service from a Chinese company, you may want to review the data handling policy when using the official app or API (self-hosting avoids this concern)
- The official API’s dedicated R1 endpoint has been retired, so the official route assumes migration to the successor V4 models
- No multimodal support (image recognition or image generation)
Comparison with Similar Services
| Comparison | DeepSeek R1 | OpenAI o3 | Gemini 2.5 Pro | Qwen (QwQ/Qwen3) |
|---|---|---|---|---|
| Provider | DeepSeek AI | OpenAI | Alibaba | |
| Model availability | Open weights (MIT) | Closed | Closed | Open weights (Apache 2.0, etc.) |
| Strengths | Math, code, cost efficiency | Overall reasoning and tool use | Long context, multimodal | Many sizes, multilingual |
| Self-hosting | Yes | No | No | Yes |
| Free usage | Chat app and weights both free | Limited | Limited | Weights free to download |
Who Is It For
- People who want AI for tasks that demand careful thinking, such as math, algorithms, and code debugging
- Companies and developers who want to self-host the model and run reasoning workloads without sending data outside
- Students and researchers who want to observe a reasoning model’s thought process and learn how AI behaves
- Indie developers who want strong reasoning performance while keeping API costs down
- Anyone looking for a lightweight reasoning model (the distilled versions) that runs on a local PC
Summary
DeepSeek R1 overturned the assumption that top-tier reasoning performance was only available from expensive closed models, showing the world an open-weights alternative. While the official API has consolidated around the successor V4 models, the MIT-licensed weights remain published, and the model stays usable via self-hosting or third-party APIs. If your work centers on math and coding, or you value data sovereignty through self-hosted operation, start by testing its performance with the chat app’s DeepThink mode or a distilled version.