DeepSeek R1
Reasoning-first MoE model optimized for multi-step logic, math, and complex planning.
- Reasoning-optimized training and evaluation.
- Reported strength on math and coding benchmarks.
- Open-source availability with multiple distilled sizes.
DeepSeek R1 is a reasoning-first MoE model designed for multi-step logic, math, and planning. Public reports describe ~671B parameters with sparse activation (~37B per token), pairing scale with practical inference cost.
R1 is open-source under MIT and ships alongside distilled variants (1.5B-70B class), enabling everything from research clusters to smaller deployments. Reported context windows range from 64K to 128K depending on variant and release notes.
Use R1 when correctness and reasoning chains matter: verification, proof-style tasks, algorithmic planning, and complex decision support. For smaller infrastructure, start with distilled models and scale up when evaluation demands it.
- Multi-step reasoning and verification.
- Distilled family for smaller deployments.
- Sparse MoE efficiency at large scale.
- Large-context variants for deep reasoning.
- Planning and structured decision workflows.