A Chinese AI lab's open-weights language models that deliver frontier-level performance at a fraction of typical inference cost.
DeepSeek is a Chinese AI research lab that builds open-weights large language models designed to match frontier performance at a fraction of the cost of closed models from OpenAI or Google. Founded in 2023 by Liang Wenfeng, the company built its reputation in January 2025 when its R1 reasoning model matched OpenAI’s o1 on many benchmarks while costing far less to run. DeepSeek’s models use a mixture of experts (MoE) architecture, which activates only a portion of the model’s total parameters for any given request, keeping inference fast and cheap without sacrificing much accuracy. For developers, DeepSeek matters because its open weights let you self-host a frontier-class model instead of depending entirely on a closed API.
DeepSeek releases its model weights publicly, meaning anyone can download, inspect, and run the models on their own hardware instead of only accessing them through an API. Its efficiency comes from how the model routes each request internally.
Mixture of experts (MoE): instead of running every parameter for every request, DeepSeek’s models split into specialized sub-networks called experts and activate only the ones relevant to the current input, cutting compute cost sharply.
V4 model family: DeepSeek V4 ships in a Flash variant tuned for cheap, high-volume workloads and a Pro variant for deeper reasoning, both supporting a 1 million token context window.
Open weights: because the weights are published under a permissive licence, teams can fine-tune or self-host the model instead of relying solely on DeepSeek’s hosted API.
Reasoning mode: DeepSeek’s R1 line is trained specifically to work through multi-step problems step by step before answering, similar in spirit to OpenAI’s o-series reasoning models.
Cached inputs: repeated or similar prompts hit a cache that costs a fraction of standard input pricing, which matters for applications that send similar context repeatedly.
Self-hosted inference backend: download the open weights and run DeepSeek V4 on your own infrastructure, avoiding per-token API costs entirely for high-volume internal tools.
Cost-sensitive coding assistant: use the cheap V4 Flash tier to power a code completion or review tool where per-request cost matters more than squeezing out the last bit of reasoning quality.
Reasoning-heavy research agent: wire up the R1 model line for tasks like proof checking, multi-step maths, or logic-heavy data analysis where step-by-step reasoning outperforms a single-shot answer.
Fine-tuned domain model: start from DeepSeek’s open weights and fine-tune on your own data to build a specialized model for a narrow task, something you cannot do with closed models.
High-volume batch processing: use the V4 Flash tier’s low per-token cost to classify, summarize, or extract data from large document sets without the API bill dominating the project budget.
DeepSeek's chat interface is free to use with standard usage limits. The API is paid but priced far below most competitors, with V4 Flash costing $0.14 per million input tokens, and the open-weights models can be downloaded and self-hosted at no licensing cost beyond your own compute.
DeepSeek's open-weights models can be downloaded and run entirely on your own infrastructure, which avoids sending data to any external server. If you use DeepSeek's hosted chat or API instead, your data is processed on DeepSeek's servers, so organizations with strict data residency requirements should evaluate that separately.
V4 is DeepSeek's general-purpose model family, split into a fast Flash tier and a more capable Pro tier. R1 is a specialized reasoning line built on top of DeepSeek's earlier models, trained to work through complex problems step by step rather than answering in a single pass.
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