Suhail launches Particle and its first model, Particle-1: a DeepSeek V4.1 Flash post-train that uses about 45% fewer tokens on coding tasks
On Oct 28, Mixpanel founder Suhail Doshi announced his new company Particle on X, with its first model, Particle-1, live now. Particle's blog says it is a reinforcement-learning post-train of DeepSeek V4.1 Flash that uses 28.4% to 52.2% fewer output tokens and costs 34.4% to 51.6% less per task across four coding and data engineering benchmarks, with pass rates inside the base model's confidence intervals. It is available through the Particle API, OpenRouter and Vercel AI Gateway; Particle's docs list $0.25 input and $1.00 output per million tokens with a 1,048,576-token context window.
Key Takeaways
- Across four benchmarks (Terminal-Bench 2.1, ReactBench, ADE Bench, DeepSWE 1.1), 28.4% to 52.2% fewer output tokens and 34.4% to 51.6% lower cost per task (vendor-reported)
- Particle's summary table: $0.2211 per task versus $0.3368 for DeepSeek V4.1 Flash and $0.6523 for GLM 5.3 Flash; 59,580 mean output tokens versus 83,246 and 98,155
- Task pass rate 72.57% versus 71.15% for the base model and 66.82% for GLM 5.3 Flash; GPQA Diamond 90.40% (base 90.91%), MMLU 86.08% (base 85.98%)
- Training: a rank-64 LoRA on the experts in all layers, about 1.25% of the model's parameters, trained with sequence-level GSPO in two RL stages
- Pricing: Particle docs currently list $0.25 input, $0.03 cached input and $1.00 output per million tokens, a 1,048,576-token context and a 131,072-token output ceiling
Linked Models & Agents:
