deepseek/deepseek-r1

deepseek/deepseek-r1 is a advanced-tier language model from OpenRouter with 671B parameters. deepseek/deepseek-r1 is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through OpenRouter's API. It is a reasoning model that uses extended chain-of-thought (CoT) processing before producing a final answer — significantly more capable on complex math, science, and multi-step logic tasks, but slower and more expensive per query than standard models. DeepSeek is a Chinese AI lab's open-weights model family, known for competitive performance at very low API prices — particularly strong on coding and math benchmarks.

deepseek/deepseek-r1 is priced at $0.55/1M input tokens and $2.19/1M output tokens. Output tokens are 4× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, deepseek/deepseek-r1 costs approximately $1.65 per 1,000 API calls. OpenRouter does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.55/1M input rate.

Its 164K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation. Median latency (P50 time-to-first-token) is approximately 920ms — better suited to batch or async workloads where latency is less critical. Throughput is approximately 75 tokens/second — adequate for streaming at comfortable reading speed. Maximum output length is 33K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

deepseek/deepseek-r1 is best suited to reasoning, coding, cost. Supported capabilities include streaming token output for real-time UIs. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how deepseek/deepseek-r1 compares to similar models across these dimensions.

deepseek/deepseek-r1 sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. As an open-weights model, deepseek/deepseek-r1 can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing deepseek/deepseek-r1 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (85/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.