DeepSeek R1
vs
deepseekv3.org
DeepSeek R1
DeepSeek R1 is a cutting-edge, open-source language model that sets new benchmarks in AI reasoning. Built with a Mixture of Experts (MoE) architecture, it features 37 billion active parameters out of 671 billion total parameters and supports a 128K context length.
This model utilizes advanced reinforcement learning techniques, enabling capabilities such as self-verification and multi-step reflection. DeepSeek R1 provides exceptional performance in mathematical reasoning, code generation, and complex problem-solving while maintaining open-source accessibility with MIT licensing.
deepseekv3.org
DeepSeek v3 represents the latest advancement in large language models, featuring a groundbreaking Mixture-of-Experts architecture with 671B total parameters. This innovative model demonstrates exceptional performance across various benchmarks, including mathematics, coding, and multilingual tasks.
Trained on 14.8 trillion diverse tokens and incorporating advanced techniques like Multi-Token Prediction, DeepSeek v3 sets new standards in AI language modeling. The model supports a 128K context window and delivers performance comparable to leading closed-source models while maintaining efficient inference capabilities.
DeepSeek R1
Pricing
deepseekv3.org
Pricing
DeepSeek R1
Features
- Architecture: MoE (Mixture of Experts) with 37B active/671B total parameters and 128K context length.
- Reinforcement Learning: Implements advanced reinforcement learning for self-verification, multi-step reflection, and human-aligned reasoning.
- Performance - Math: 97.3% accuracy on MATH-500.
- Performance - Coding: Outperforms 96.3% of Codeforces participants.
- Performance - General Reasoning: 79.8% pass rate on AIME 2024 (SOTA).
- Deployment - API: OpenAI-compatible endpoint ($0.14/million tokens).
- Open Source: MIT-licensed weights, 1.5B-70B distilled variants for commercial use.
- Model Ecosystem: Base (R1-Zero), Enhanced (R1), and 6 lightweight distilled models.
deepseekv3.org
Features
- Advanced MoE Architecture: Utilizes an innovative Mixture-of-Experts architecture with 671B total parameters, activating 37B parameters for each token for optimal performance.
- Extensive Training: Pre-trained on 14.8 trillion high-quality tokens, demonstrating comprehensive knowledge across various domains.
- Superior Performance: Achieves state-of-the-art results across multiple benchmarks, including mathematics, coding, and multilingual tasks.
- Efficient Inference: Maintains efficient inference capabilities through innovative architecture design, despite its large size.
- Long Context Window: Features a 128K context window to process and understand extensive input sequences effectively.
- Multi-Token Prediction: Incorporates advanced Multi-Token Prediction for enhanced performance and inference acceleration.
DeepSeek R1
Use cases
- Complex problem-solving
- Mathematical modeling and reasoning
- Production-grade code generation
- Multilingual natural language understanding
- AI research
- Enterprise applications requiring advanced reasoning
deepseekv3.org
Use cases
- Text generation
- Code completion
- Mathematical reasoning
- Multilingual tasks
DeepSeek R1
FAQs
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How does DeepSeek R1 compare to OpenAI o1 in pricing?
DeepSeek R1 costs 90-95% less: $0.14/million input tokens (cache hit) vs OpenAI o1's $15, with equivalent reasoning capabilities.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.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.Where can I find technical documentation for DeepSeek R1?
Access full specs via the DeepSeek R1 Technical Paper and API docs.
deepseekv3.org
FAQs
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What makes DeepSeek v3 unique?
DeepSeek v3 combines a massive 671B parameter MoE architecture with innovative features like Multi-Token Prediction and auxiliary-loss-free load balancing, delivering exceptional performance across various tasks.How can I access DeepSeek v3?
DeepSeek v3 is available through our online demo platform and API services. You can also download the model weights for local deployment.What frameworks are supported for DeepSeek v3 deployment?
DeepSeek v3 can be deployed using multiple frameworks including SGLang, LMDeploy, TensorRT-LLM, vLLM, and supports both FP8 and BF16 inference modes.Is DeepSeek v3 available for commercial use?
Yes, DeepSeek v3 supports commercial use subject to the model license terms.How was DeepSeek v3 trained?
DeepSeek v3 was pre-trained on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages. The training process was remarkably stable with no irrecoverable loss spikes.
DeepSeek R1
Uptime Monitor
Average Uptime
99.95%
Average Response Time
62.4 ms
Last 30 Days
deepseekv3.org
Uptime Monitor
Average Uptime
99.85%
Average Response Time
266.17 ms
Last 30 Days
DeepSeek R1
deepseekv3.org
