deepseek-r1

DeepSeek-R1 is a reasoning model from DeepSeek that uses reinforcement learning to develop chain-of-thought reasoning capability, achieving competitive performance on mathematics and coding benchmarks at a fraction of the cost of proprietary reasoning models. It is an open-weights model (MIT license), enabling self-hosting, fine-tuning, and commercial deployment without per-token API fees.

DeepSeek-R1 is available at approximately $0.55 per million input tokens and $2.19 per million output tokens from DeepSeek's own API, and at similar rates from third-party providers. At 1,000 input + 500 output tokens per call, it costs approximately $0.0016 per 1,000 API calls — significantly cheaper than proprietary reasoning models at comparable capability levels. Self-hosted deployments (the full 671B MoE model) require substantial GPU infrastructure — typically 8× H100 80GB GPUs — but eliminate per-token costs entirely. Distilled variants (1.5B to 70B) are available for more accessible self-hosting at reduced capability.

DeepSeek-R1 supports a 128K-token context window. API latency varies — DeepSeek's own API has historically had higher latency and lower reliability than US-based providers, with TTFT ranging from 1 to 30+ seconds depending on load. Third-party providers (Together AI, Fireworks, etc.) offer more consistent latency. Throughput on DeepSeek's API is approximately 20–40 tokens per second for visible output. The full 671B MoE model activates approximately 37B parameters per token, making self-hosted inference more feasible than a dense 671B model. Maximum output is 8,192 tokens.

DeepSeek-R1 is best suited to mathematics, competitive programming, scientific reasoning, and any task where chain-of-thought reasoning improves accuracy. On advanced mathematics and competitive programming benchmarks, it performs comparably to leading proprietary reasoning models at a fraction of the API cost — a compelling value proposition for reasoning-heavy workloads. Its MIT license makes it uniquely flexible for commercial applications: it can be fine-tuned, modified, and deployed without the licensing restrictions of other open-weights models. For teams with GPU infrastructure and high-volume reasoning workloads, self-hosting R1 or its distilled variants can deliver substantial cost savings.

DeepSeek-R1 sits in the frontier reasoning tier, competing with proprietary reasoning models from OpenAI and Anthropic. Its main advantages are cost (significantly cheaper than proprietary alternatives) and open-weights flexibility (MIT license, self-hostable). Its main disadvantages are API reliability (DeepSeek's own infrastructure has had availability issues) and data privacy concerns for sensitive workloads (data processed by DeepSeek's API is subject to Chinese data regulations). For teams comfortable with self-hosting or using US-based third-party providers, R1 offers exceptional reasoning capability per dollar. Its intelligence score of approximately 88/100 reflects near-frontier reasoning performance.