Particle-1 released: Optimized based on DeepSeek, its task success rate is close to the original version, with output tokens reduced by approximately 45%
1 hours ago
Insight Beating AI Flash News: Suhail Doshi, co-founder of product analytics platform Mixpanel and founder of AI image platform Playground, has launched a new venture called Particle, debuting its first model, Particle-1. Trained via reinforcement learning on DeepSeek V4.1 Flash, the model is designed to reduce token consumption during coding agent tasks. Four programming and data engineering benchmarks released by Particle show that Particle-1 cuts output tokens by 28.4% to 52.2% compared to the original DeepSeek model, with an overall average reduction of around 45%, while task pass rates stay within the original model’s statistical confidence interval. Estimated at the same token unit price and fixed cache hit rate, per-task costs drop by 34.4% to 51.6%. The token savings stem mainly from more streamlined inference, fewer retries, and early termination after task completion. In a Git deployment test, both models successfully finished the task: Particle-1 output only 15,892 tokens, while the original model used 47,006 tokens. Particle-1 has launched an open API and integrated with Vercel AI Gateway. Current performance and cost data are primarily from the team’s internal testing.
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