Parameter count isn't the whole story here. Llama 3.1 405B Instruct is a 405B dense model — the largest open-weights model from Meta's 3.1 generation — with a 128K context window and strong across-the-board benchmarks. DeepSeek R1 is purpose-built for reasoning, using reinforcement learning to develop explicit chain-of-thought capability regardless of raw parameter scale. On hosted inference, Llama 3.1 405B is one of the more expensive open-weights models to serve: dense 405B requires significant GPU RAM, and providers reflect that with $1.00–$3.00/1M input token pricing. DeepSeek R1, as a MoE-derived reasoning model, can be more cost-competitive on some providers in the $0.50–$1.50/1M range — though the extended thinking tokens add cost back in. [DeepSeek R1](/models/deepseek--deepseek-r1) wins on formal reasoning tasks: AIME 2024, MATH-500, multi-step algorithm derivation. Its reasoning benchmark scores exceed Llama 3.1 405B despite nominally fewer parameters, which is the empirical case for RL-trained reasoning over brute-force scale. [Llama 3.1 405B Instruct](/models/meta--llama-3.1-405b-instruct) holds the edge for broad knowledge tasks, long-context retrieval over 100K-token documents, and workloads that benefit from the dense model's general fluency and instruction-following polish. It's the better pick for creative synthesis, nuanced summarization, and tasks where you're not specifically optimizing for multi-step logic. Pick DeepSeek R1 for reasoning-first workloads where you're benchmarking on math or logic. Pick Llama 3.1 405B Instruct for knowledge-intensive or long-context tasks where the dense parameter mass earns its cost.