DeepSeek R1 Online (Free|Nologin)
Revolutionary Open-Source AI Model for Advanced Reasoning that beats Openai o1
DeepSeek R1 Chat online free
DeepSeek R1 WEBGPU Online
A next-generation reasoning model that runs locally in your browser with WebGPU acceleration.
You are about to load DeepSeek-R1-Distill-Qwen-1.5B, a 1.5B parameter reasoning LLM optimized for in-browser inference. Everything runs entirely in your browser with 
Transformers.js and ONNX Runtime Web, meaning no data is sent to a server. Once loaded, it can even be used offline.
AI Coding Agent Powered BY DeepSeek online Free Now！
Boltnew.ai is powered By deepseek V3 , is a code generation tool ,its free now!
Feature Packed Of DeepSeek R1 online
Architecture
Built on MoE (Mixture of Experts) with 37B active/671B total parameters and 128K context length. Implements advanced reinforcement learning to achieve self-verification, multi-step reflection, and human-aligned reasoning capabilities.
Performance
Math: 97.3% accuracy on MATH-500
Coding: Outperforms 96.3% of Codeforces participants
General Reasoning: 79.8% pass rate on AIME 2024 (SOTA)
These results position DeepSeek R1 among the top-performing AI models globally.
Deployment
API: OpenAI-compatible endpoint ($0.14/million tokens)
Open Source: MIT-licensed weights, 1.5B-70B distilled variants for commercial use.
Find it in GitHub Repository
Model Ecosystem
Variants: Base (R1-Zero), Enhanced (R1), 6 lightweight distilled models
Specialization: Optimized for complex problem-solving, multilingual understanding, and production-grade code generation
Roadmap
Continuous upgrades for multimodal support, conversational enhancement, and distributed inference optimization, driven by open-source community collaboration.
Open Source
World’s first pure RL-developed reasoning model with open-source implementation 32B lightweight version achieves GPT-4-level math performance at 90% lower cost
Chain-of-Thought visualization capability, addressing AI “black box” challenges
What is DeepSeek R1 online?
DeepSeek R1 represents a groundbreaking advancement in artificial intelligence, offering state-of-the-art performance in reasoning, mathematics, and coding tasks. This innovative model demonstrates capabilities comparable to leading proprietary solutions while maintaining complete open-source accessibility.
Technical Architecture and Capabilities
Model Architecture
DeepSeek R1 utilizes a sophisticated MoE (Mixture of Experts) architecture with:
- 37B activated parameters
- 671B total parameters
- 128K context length support
The DeepSeek R1 framework incorporates advanced reinforcement learning techniques, setting new benchmarks in AI reasoning capabilities.
Performance Benchmarks
DeepSeek R1 has achieved remarkable results across various benchmarks:
- MATH-500: 97.3% accuracy
- AIME 2024: 79.8% pass rate
- Codeforces: 96.3% percentile ranking
These results position DeepSeek R1 among the top-performing AI models globally.
Model Variants and Distillation of Deepseek online
Available Versions
DeepSeek R1 comes in multiple variants:
- DeepSeek R1-Zero: Base model
- DeepSeek R1: Enhanced version
- Multiple distilled versions ranging from 1.5B to 70B parameters
Performance Optimization
The model demonstrates exceptional capabilities in:
Complex problem-solving
Mathematical reasoning
Code generation
Natural language understanding
DeepSeek-R1-Distill Models (download online)
| Model | Base Model | Download |
|---|---|---|
| DeepSeek-R1-Distill-Qwen-1.5B | Qwen2.5-Math-1.5B | HuggingFace |
| DeepSeek-R1-Distill-Qwen-7B | Qwen2.5-Math-7B | HuggingFace |
| DeepSeek-R1-Distill-Llama-8B | Llama-3.1-8B | HuggingFace |
| DeepSeek-R1-Distill-Qwen-14B | Qwen2.5-14B | HuggingFace |
| DeepSeek-R1-Distill-Qwen-32B | Qwen2.5-32B | HuggingFace |
| DeepSeek-R1-Distill-Llama-70B | Llama-3.3-70B-Instruct | HuggingFace |
you can find more info about DeepSeek-R1-Distill Models here
Pricing of Deepseek R1
Pricing Detail
| MODEL(1) | CONTEXT LENGTH | MAX COT TOKENS(2) | MAX OUTPUT TOKENS(3) | 1M TOKENS INPUT PRICE (CACHE HIT) (4) | 1M TOKENS INPUT PRICE (CACHE MISS) | 1M TOKENS OUTPUT PRICE |
|---|---|---|---|---|---|---|
| deepseek-chat | 64K | – | 8K | $0.014 | $0.14 | $0.28 |
| deepseek-reasoner | 64K | 32K | 8K | $0.14 | $0.55 | $2.19 (6) |
Price Comparison: DeepSeek R1 vs. OpenAI o1
1. DeepSeek R1 Pricing
DeepSeek R1 offers a highly competitive pricing structure, making it significantly more affordable than OpenAI o1:
- Input Tokens (Cache Hit): $0.14 per million tokens
- Input Tokens (Cache Miss): $0.55 per million tokens
- Output Tokens: $2.19 per million tokens
The intelligent caching system reduces costs for repeated queries, providing up to 90% savings for cache hits25.
2. OpenAI o1 Pricing
In contrast, OpenAI o1 is considerably more expensive:
- Input Tokens: $15 per million tokens
- Output Tokens: $60 per million tokens
This makes OpenAI o1 90-95% more costly than DeepSeek R1 for equivalent usage112.
3. Cost Efficiency
DeepSeek R1’s pricing is 90-95% lower than OpenAI o1, offering a cost-effective alternative without compromising performance. For example:
- 1 Million Input Tokens:
- DeepSeek R1: 0.14(cachehit)or0.14(cachehit)or0.55 (cache miss)
- OpenAI o1: $15
- 1 Million Output Tokens:
- DeepSeek R1: $2.19
- OpenAI o1: $60
This affordability makes DeepSeek R1 an attractive choice for developers and enterprises1512.
4. Additional Benefits
- Open-Source Access: DeepSeek R1 is available under an MIT license, allowing free use, modification, and commercialization512.
- API Flexibility: DeepSeek R1’s API supports advanced features like chain-of-thought reasoning and long-context handling (up to 128K tokens)212.
Blogs And News about Deepseek R1 and Deepseek online
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DeepSeek has released its source code, detailed explanation of FlashMLA
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What is FlashMLA? A Comprehensive Guide to Its Impact on AI Decoding Kernels
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Qwen2.5-max vs DeepSeek R1: A deep comparison of models: a full analysis of application scenarios
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It is close to DeepSeek-R1-32B and crushes Fei-Fei Li’s s1! UC Berkeley and other open source new SOTA inference models
1,What makes DeepSeek-R1’s architecture unique?
- DeepSeek R1 uses a MoE system with 37B active/671B total parameters and 128K context support, optimized through pure reinforcement learning without supervised fine-tuning.
2. How does DeepSeek R1 compare to OpenAI o1 in pricing?
- DeepSeek R1 costs 90-95% less: 0.14/millioninputtokensvsOpenAIo1′s0.14/millioninputtokensvsOpenAIo1′s15, with equivalent reasoning capabilities.
3. Can I deploy DeepSeek R1 locally?
- Yes, DeepSeek R1 supports local deployment via vLLM/SGLang and offers 6 distilled models (1.5B-70B parameters) for resource-constrained environments.
4. What benchmarks prove DeepSeek R1’s performance?
- Achieves SOTA in MATH-500 (97.3%), Codeforces (96.3% percentile), and AIME 2024 (79.8%), outperforming most commercial models.
5. Is DeepSeek R1 open source?
- Yes, DeepSeek R1 is MIT-licensed with full model weights available on GitHub, allowing commercial use and modification.
6. What cognitive abilities distinguish DeepSeek R1?
- Features self-verification and multi-step reflection, solving complex problems through visible chain-of-thought reasoning.
7. Which industries benefit most from DeepSeek R1?
- Ideal for AI research, enterprise code generation, mathematical modeling, and multilingual NLP applications requiring advanced reasoning.
8. How does DeepSeek R1 handle API integration?
- Offers OpenAI-compatible API endpoints with 128K context support and intelligent caching ($0.14/million tokens for cache hits).
9. What safety measures does DeepSeek R1 implement?
- Built-in repetition control (temperature 0.5-0.7) and alignment mechanisms prevent endless loops common in RL-trained models.
10. Where can I find technical documentation for DeepSeek R1?
Access full specs via the DeepSeek R1 Technical Paper and API docs.
