Particle-1: DeepSeek V4.1 Flash Post-Trained to Cut Task Cost by Two Thirds

Particle AI launched its first model, Particle-1, on 28 October 2026: DeepSeek V4.1 Flash post-trained with reinforcement learning for token efficiency. Particle reports roughly 45% fewer tokens on average, 28.4%–52.2% fewer output tokens and 34.4%–51.6% lower cost per task across four software-engineering benchmarks, with pass rates inside the base model's confidence intervals

Tool Interface

Interactive tool will be available soon

Features

✓ The goal is token economy, not leaderboard vanity: Particle reports roughly 45% fewer tokens on average and 28.4%–52.2% fewer output tokens across four benchmarks
✓ In money: Particle's own table shows $0.2211 average cost per task versus $0.3368 for DeepSeek V4.1 Flash and $0.6523 for GLM 5.3 Flash
✓ Accuracy holds: Particle reports a 72.57% task pass rate against 71.15% for the base model, inside the base model's confidence intervals
✓ Training ran in Particle's own reinforcement learning environments, with a reward that pays for both solving the task and spending fewer reasoning and tool-call tokens
✓ Scope is deliberately narrow at launch: Terminal-Bench 2.1, ReactBench, ADE Bench and DeepSWE 1.1, all software engineering, with other domains promised for later releases

How to Use

Check whether cost is actually your bottleneck: this model optimises token spend, so cheap workloads see little benefit
Pick one repeatable task and A/B it: the same issues or the same terminal workflow through Particle-1 and DeepSeek V4.1 Flash
Track three numbers together: task pass, output tokens, cost per task. Watching only one of them will mislead you
Confirm the pass-rate delta is acceptable before production, because Particle itself frames the gain as efficiency rather than a quality guarantee

FAQ

When did it launch?

28 October 2026, as Particle AI's first model. Official post: https://particle.ai/blog/introducing-particle-1 .

Fewer tokens than what?

Than its base, DeepSeek V4.1 Flash. Particle quotes roughly 45% as an average across a wide variety of tasks, with a per-task range of 28.4%–52.2% fewer output tokens and 34.4%–51.6% lower cost.

Does accuracy really hold?

Particle's claim is that pass@1 stays within the base model's confidence intervals and cites 72.57% against 71.15%. That is vendor-reported, so run your own comparison before trusting it on your workload.

How do I call it?

Through Particle's API, and it also appears on OpenRouter (https://openrouter.ai/particle-ai/particle-1) and in Vercel's AI Gateway catalogue. Check each listing for current pricing.

Can it do anything besides software engineering?

Particle says the first release focuses on software engineering with other domains planned. On general benchmarks it only reports staying close to base, for example GPQA Diamond 90.40% versus 90.91%.