Cognition releases SWE-grep models for fast parallel code context retrieval
Original titleIntroducing SWE-grep and SWE-grep-mini: RL for Multi-Turn, Fast Context Retrieval
Cognition introduces SWE-grep and SWE-grep-mini, fast agentic models trained with reinforcement learning for multi-turn context retrieval in coding tasks.
The company says they match frontier coding models at retrieval while taking an order of magnitude less time, and they power the Fast Context subagent in Windsurf.
The models issue up to 8 parallel tool calls per turn within 4 turns, and Cerebras serves SWE-grep-mini at over 2,800 tokens per second and SWE-grep at over 650 tokens per second.
The post explains the speed-intelligence tradeoff in agentic code search, showing how parallel tool calls and RL training change the cost of retrieving context for coding agents.
Source: Cognition Blog (Devin, Windsurf) · cognition.comPublished · added here