What is DeepSeek
DeepSeek stands out because it has become a reference point for price-performance. Teams exploring DeepSeek are usually asking a practical question: can we get strong reasoning and coding output at materially lower API cost than the biggest frontier vendors?
DeepSeek is a newer entrant than many of the largest Western AI brands, but it rose quickly by becoming shorthand for strong reasoning performance at disruptive API prices. Its history matters because the company’s momentum has come less from consumer branding and more from changing the market conversation around price-performance for serious model use.
Core offerings
- Chat and reasoning model options for general assistant and analytical workloads.
- Developer-facing API access with public pricing.
- A reputation for strong reasoning value relative to cost.
Pricing
DeepSeek’s public API documentation currently lists deepseek-chat at US$0.27 per million input tokens on a cache miss and US$1.10 per million output tokens. deepseek-reasoner is listed at US$0.55 per million input tokens on a cache miss and US$2.19 per million output tokens. DeepSeek also publicised cache-hit pricing reductions that can cut repeat-context costs sharply.
Model footprint
DeepSeek’s public pricing pages are comparatively simple, with core attention on chat and reasoning models rather than a sprawling consumer product catalogue. That simplicity is a strength for builders who care about predictable deployment decisions.
Why select DeepSeek
DeepSeek is compelling when cost matters, reasoning matters, and you are comfortable with a more focused platform. It is particularly attractive for experimentation, agent backends, coding assistants, and workloads where premium model prices have blocked broader rollout.
Official sources: DeepSeek API pricing, DeepSeek context caching note.
Current models
DeepSeek’s current public pricing page is unusually clean and useful. It exposes live model versions, context windows, max output, thinking support, concurrency, and cache-hit pricing directly.
| Model | Context / output | Knowledge / training | Pricing / token use | Speed / notes |
|---|---|---|---|---|
DeepSeek-V4-Flashdeepseek-v4-flash | 1M context 384K max output | Thinking and non-thinking modes supported Training data and cutoff: not publicly disclosed | US$0.14 input cache miss / US$0.0028 cache hit / US$0.28 output per 1M tokens | Aggressively priced, high-concurrency model with JSON, tool calls, prefix completion, and FIM support |
DeepSeek-V4-Prodeepseek-v4-pro | 1M context 384K max output | Thinking and non-thinking modes supported Training data and cutoff: not publicly disclosed | US$0.435 input cache miss / US$0.003625 cache hit / US$0.87 output per 1M tokens | Higher-priced Pro tier with lower concurrency ceiling than Flash |
Compatibility aliasesdeepseek-chat / deepseek-reasoner | Alias layer over V4 Flash modes | Non-thinking and thinking compatibility routes only | Compatibility layer rather than separate long-term models | Useful to track because legacy app integrations may still reference them |