DeepSeek R1
Open-source reasoning model matching o1-level performance at a fraction of the cost. Ideal for math, coding, logic.
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Tips to reduce cost
- →Use prompt caching to reuse repeated system prompts
- →Trim whitespace and reduce verbose instructions
- →Use a smaller model for classification or routing tasks
- →Batch async requests to get 50% discount (OpenAI/Anthropic)
- →Cache identical requests at the application layer
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Compared at your current token settings
About DeepSeek R1
DeepSeek R1 is a mid-range large language model from deepseek, priced at $0.55/1M input tokens and $2.19/1M output tokens. It is 79% cheaper than the market average and best suited for math & logic reasoning. The 64k context window covers most standard production workloads.
As a reasoning model, DeepSeek R1 generates internal thinking tokens before responding. These are billed at the output token rate and can add 2–4x to effective output cost. For tasks requiring deep reasoning — math, complex coding, multi-step analysis — this overhead is usually justified by fewer errors and retries.
DeepSeek R1 supports prompt caching at $0.14/1M — a 75% discount on repeated input tokens. For applications with a fixed system prompt or repeated document context (RAG, chatbots, agents), enabling caching is the single highest-leverage cost optimization available.