Updated

Particle-1

Particle launched Particle-1, a model post-trained from DeepSeek V4.1 Flash and initially focused on software engineering. Particle says its training approach reduces token use while keeping task success within the base model’s reported confidence intervals on evaluated tasks.

By No Code MBA · Facts checked

Provider
Particle
Announcement date
Status at last check
Generally available

What changed

Particle-1 is post-trained from DeepSeek V4.1 Flash using reinforcement learning environments developed by Particle. Particle reports about 45% fewer tokens on average across a range of tasks, with pass rates within the base model’s confidence intervals on its reported evaluations.

Access and availability

Create a Particle API key through the Particle platform, then use the particle-1 model alias through the OpenAI-compatible Particle API. Inference requires a positive prepaid credit balance unless the key has free access.

Official access documentation

What you can build

Compare it with your current coding model on a small, repeatable software task, tracking both completion quality and token use rather than treating efficiency claims as a quality guarantee.

Limitations

The initial release focuses on software engineering. Token-efficiency and task-performance results are Particle’s own evaluations and are not independent test results.

Sources and release history