DeepSeek R1 is an open-source advanced reasoning AI model. The official site provides a free, no-login online chat entry point and supports running a lightweight version of the model locally in the browser via WebGPU. The following content is compiled from publicly available information on the site.
[Purpose and Positioning] DeepSeek R1 is aimed at tasks requiring complex reasoning, mathematical computation, code generation, and natural language understanding. The official site states that its reasoning capability reaches or exceeds the level of OpenAI o1, and it is provided in a fully open-source form for research, development, and enterprise use.
[Main Capabilities]
- Architecture: Uses a MoE (Mixture of Experts) architecture, with 37B activated parameters, 671B total parameters, and support for a 128K context length.
- Reasoning mechanism: Achieves self-verification, multi-step reflection, and human-aligned reasoning capabilities through pure reinforcement learning, and provides chain-of-thought visualization.
- Benchmark performance: MATH-500 accuracy 97.3%; AIME 2024 pass rate 79.8%; Codeforces percentile ranking 96.3%.
- Model versions: Includes the base version R1-Zero, the enhanced version R1, and multiple distilled models ranging from 1.5B to 70B.
- Local browser operation: The site provides a WebGPU online experience for DeepSeek-R1-Distill-Qwen-1.5B, based on 🤗 Transformers.js and ONNX Runtime Web. Data is not sent to a server, and it can be used offline after loading.
- API: Provides an OpenAI-compatible endpoint, supporting 128K context and intelligent caching.
[How to Use]
- Online chat: Use it directly through the free, no-login entry point provided on the website.
- Local browser inference: Use WebGPU acceleration to run the 1.5B distilled model in the browser, with offline support after the first load.
- API access: Use the OpenAI-compatible interface, supporting billing for cache hits and misses.
- Local deployment: Supports local deployment via vLLM/SGLang.
- Model download: MIT-licensed weights can be obtained from the GitHub repository, including multiple distilled versions.
[Applicable Scenarios]
- AI research and complex problem solving
- Enterprise-level code generation and programming assistance
- Mathematical modeling and reasoning tasks
- Multilingual natural language processing
- Scenarios requiring long-context processing
[Limitations and Notes]
- The site does not provide specific details on safety measures, mentioning only built-in repetition control and temperaturesettings.
- The local browser version only supports the 1.5B distilled model, not the full 671B model.
- Some blog post dates show future times (such as 2026), which may be sample content on the website.
- Functionality and performance data come from the site's own statements, with no third-party verification information provided.
[Pricing] API pricing: input tokens (cache hit) $0.14/million tokens; input tokens (cache miss) $0.55/million tokens; output tokens $2.19/million tokens. The site claims it is 90-95% cheaper than OpenAI o1.
