DeepSeek is a Chinese AI lab delivering open source language models focused on efficient reasoning and coding. Its flagship DeepSeek R1 matches top performers like OpenAI’s o1 in benchmarks for math and programming, using reinforcement learning to show step by step thought processes.

The architecture employs mixture of experts with 236 billion total parameters but activates only 21 billion per token, cutting compute needs dramatically. This setup allows training costs under six million dollars, far below industry norms. DeepSeek V3 supports 128K token contexts, making it ideal for long document analysis. Also, the API charges low rates, around 0.14 dollars per million input tokens, making it accessible for developers throughout the world.

Users appreciate the models transparency in reasoning traces, which aids debugging and learning. However, it shows occasional biases in sensitive topics due to training data.

DeepSeek often matches or even edges out ChatGPT o3 in and Claude benchmarks such as SWE-bench while costing just 1/20th as much per token, though it lags slightly in multimodal tasks and creative flair. Compared to open-source peers, it crushes Llama and Mistral in efficiency and raw intelligence for developer workflows, powering everything from agentic coding to crypto trading simulations. That said, users on Reddit note its occasional service hiccups, but for budget-conscious builders – DeepSeek probably feels like the smart disruptor shaking up 2025’s AI game.

The OCR feature compresses text via images, processing thousands of pages efficiently on standard hardware. Prover V2 handles theorem proving with high accuracy on AIME tests. Distilled versions like R1 8B run on single GPUs, broadening deployment options.

For coding, it generates clean files with accompanying tests that pass on the first run. In agentic workflows, V3.1 Terminus improves tool integration, though complex chains may falter.

Overall, DeepSeek prioritizes efficiency and openness, thus appealing to cost conscious builders. To get started, download weights from Hugging Face and test via their web playground before integrating into projects.