Particle-1

Particle-1 is Particle AI's first model, a reinforcement-learning post-train of DeepSeek V4.1 Flash (rank-64 LoRA over the experts in all layers, trained with GSPO in Cascade RL stages on internal coding and data-engineering environments). It targets agentic software engineering and is tuned to solve tasks with fewer tokens: Particle reports 28.4% to 52.2% fewer output tokens and 34.4% to 51.6% lower cost per task than the base model on Terminal-Bench 2.1, ReactBench, ADE Bench, and DeepSWE 1.1, with pass@1 within the base model's confidence intervals (72.57% aggregate task pass rate vs 71.15% for DeepSeek V4.1 Flash). Text and image input, text output, reasoning with effort control, OpenAI-compatible tool calling, 1,048,576-token context and 131,072 max output tokens. Weights are not released; it is available through the Particle API and OpenRouter (particle-ai/particle-1) at $0.25 input, $0.03 cached input, and $1.00 output per million tokens (peak; off-peak 50% off, 20:30 to 04:00 America/New_York).