DeepSeek R1 Guide: Architecture, Benchmarks, and Practical Usage in 2026

DeepSeek R1 Guide: Architecture, Benchmarks, and Practical Usage in 2026 DeepSeek R1 proved that open-source models can match closed-source reasoning capabilities. Released in January 2025 under the MIT license, it scores 79.8% on AIME 2024 and 97.3% on MATH-500, putting it in the same tier as OpenAI's o1 series. A year later, R1 remains one of the most cost-effective reasoning models available. At $0.55/$2.19 per 1M tokens, it's 5-10x cheaper than comparable closed-source alternatives. Here's what you need to know to use it effectively. Architecture: Why 671B Parameters Doesn't Mean 671B Cost DeepSeek R1 uses a Mixture of Experts (MoE) architecture: 671 billion total parameters 37 billion activated per forward pass Built on DeepSeek-V3-Base foundation 128K token context window The MoE design means R1 has the knowledge capacity of a 671B model but the inference cost of a ~37B model. Each input token activates only a subset of "expert" networks, keeping compute requirements manageable.