Jan-nano Technical Report
Authors: name: Alan Dao, name: Dinh Bach Vu

Most language models face a fundamental tradeoff where powerful capabilities
require substantial computational resources. We shatter this constraint with
Jan-nano, a 4B parameter language model that redefines efficiency through
radical specialization: instead of trying to know everything, it masters the
art of finding anything instantly. Fine-tuned from Qwen3-4B using our novel
multi-stage RLVR system that completely eliminates reliance on next token
prediction training (SFT), Jan-nano achieves 83.2% on SimpleQA benchmark with
MCP integration while running on consumer hardware. With 128K context length,
Jan-nano proves that intelligence isn't about scale, it's about strategy.