DeepSeek vs Perplexity API
Comparing two ai & llm apis platforms on pricing, features, free tier, and trade-offs.
Quick summary
DeepSeek — Chinese open-weight frontier models. DeepSeek R1 is an open-weight reasoning model competitive with OpenAI's o1, at a fraction of the price. DeepSeek V3 is a strong general-purpose LLM.
Perplexity API — LLM with live web search built in. Perplexity API (Sonar) gives LLM answers grounded in real-time web search results, with citations. Great for up-to-date answers and research use cases.
Feature comparison
| Feature | DeepSeek | Perplexity API |
|---|---|---|
| Pricing model | Paid | Paid |
| Starting price | Pay per token (cheap) | Pay per token |
| Free tier | No | No |
| Open source | Yes | No |
| Vision | No | No |
| Streaming | Yes | Yes |
| Embeddings | No | No |
| Max Output | 8K | 4K |
| Fine-tuning | No | No |
| Context Window | 128K | 200K |
| Flagship Model | DeepSeek V3 | Sonar Large |
| Reasoning Model | DeepSeek R1 | Sonar Reasoning |
| Function Calling | Yes | No |
| EU Data Residency | No | No |
DeepSeek
Chinese open-weight frontier models
Pros
- Frontier reasoning at ~5% of OpenAI prices
- Open weights — can self-host
- Very competitive benchmarks
Cons
- China-based (geopolitical/compliance concerns for some)
- No vision yet
- Smaller SDK ecosystem
Perplexity API
LLM with live web search built in
Pros
- Built-in real-time web search
- Citations with every answer
- Always up-to-date information
- No need for your own scraper
Cons
- No vision / function calling
- More expensive than raw LLM APIs
- Less control over grounding data
Which should you choose?
Choose DeepSeek if you value open source and want the option to self-host, and you need production-grade features and are ready to pay. Choose Perplexity API if you need production-grade features and are ready to pay.