DeepSeek
Chinese AI research lab building open-source reasoning and general-purpose models with industry-leading cost efficiency
Overview
DeepSeek is a Chinese AI research company that has disrupted the foundation model landscape with remarkably cost-efficient open-source models. Their DeepSeek-V3 (671B parameters with MoE architecture) and DeepSeek-R1 (reasoning model) compete with GPT-4 and Claude while costing a fraction to run. The company, backed by quantitative hedge fund High-Flyer, released fully open weights under MIT license, enabling self-hosting and fine-tuning. DeepSeek's models excel particularly in mathematics, coding, and reasoning tasks, achieving state-of-the-art results on benchmarks like MATH and HumanEval while being 10-50x cheaper than comparable closed models.
The Verdict
Who Should Use DeepSeek?
Best For
- Cost-conscious API users needing GPT-4 quality
- Math and reasoning applications
- Self-hosting enthusiasts (open weights)
- Code generation tasks
- Research and fine-tuning projects
Not Ideal For
- Users requiring US/EU data residency
- Enterprises with strict compliance needs
- Applications requiring consistent uptime SLAs
- Use cases needing real-time streaming
What's Great
- 10-50x cheaper than GPT-4/Claude for similar quality
- Fully open weights under MIT license
- State-of-the-art math and reasoning performance
- R1 reasoning model rivals o1 at fraction of cost
- Excellent code generation (HumanEval 90%+)
- Active research with regular model releases
Watch Out For
- Data processed in China (compliance concerns)
- Censorship on politically sensitive topics
- API availability can be inconsistent
- Limited enterprise support options
- Slower inference than smaller models
Pricing
View all features & details
Available Models
- DeepSeek-V3 — 671B MoE, general-purpose flagship
- DeepSeek-R1 — Reasoning model, chain-of-thought
- DeepSeek-R1-Distill — Smaller reasoning variants (7B-70B)
- DeepSeek-Coder-V2 — Code-specialized model
- DeepSeek-V2.5 — Balanced performance/cost
Technical Specs
- 128K context window
- Mixture of Experts architecture
- 37B active parameters per token (V3)
- Multi-head Latent Attention (MLA)
- FP8 mixed precision training
Access Methods
- Official API (OpenAI-compatible)
- Web chat interface
- HuggingFace model downloads
- ollama, vLLM, SGLang support
- Third-party providers (OpenRouter, Together)
Licensing
- Model weights: MIT License
- Commercial use permitted
- Fine-tuning allowed
- No usage restrictions
Benchmarks
Real-World Usage
Community Adoption
- 10M+ HuggingFace downloads (V3)
- Strong community adoption across repos
- Integrated in 100+ inference platforms
- Top model on OpenRouter by usage
Cost Comparison
- V3: ~25x cheaper than GPT-4 Turbo
- R1: ~10x cheaper than o1-preview
- Self-hosted: ~$0.02/M on consumer GPUs
- Training cost: $5.6M (vs $100M+ for GPT-4)
How It Compares
| Feature | DeepSeek V3/R1 | Llama 3.1 405B | Qwen 2.5 72B | Mistral Large |
|---|---|---|---|---|
| Parameters | 671B MoE | 405B Dense | 72B Dense | 123B MoE |
| Context | 128K | 128K | 128K | 128K |
| Open Weights | MIT License | Llama License | Apache 2.0 | Proprietary |
| MMLU | 90.2% | 88.6% | 85.3% | 84.0% |
| MATH | 79.8% | 73.8% | 71.4% | 69.2% |
| API Cost (Input) | $0.14/M | $3.00/M | $0.80/M | $2.00/M |
| Reasoning Model | R1 (97% MATH) | None | QwQ | None |
| Best For | Cost efficiency | Meta ecosystem | Multilingual | EU compliance |