Particle-1 (DeepSeek V4.1 Flash): 45% Fewer Tokens, Price & API

Particle introduced Particle-1 on October 28, 2026. Particle-1 is DeepSeek V4.1 Flash trained to think less and stop sooner: it uses about 45% fewer tokens on average with the same accuracy, so coding agents cost less and finish faster.

Founder Suhail Doshi, who co-founded Mixpanel and built Playground, announced it on X as "live now". The first release is aimed at software engineering: terminal work, React apps, data engineering and long codebase tasks.

1. Particle-1 at a glance

2. How Particle-1 saves ~45% of DeepSeek tokens

Particle trained the model in its own reinforcement-learning environments. The reward is simple: solve the task, and use fewer tokens for reasoning and tool calls. Only a small LoRA adapter, about 1.25% of the model's parameters, was trained.

Comparing the same tasks where both models passed, Particle found four habits that changed:

• Less thinking for the same steps. Most savings, about 90% on three benchmarks, come from shorter reasoning.
• Stop when done. Final checks shrank a lot: in one Git task, two confirmation calls dropped from 11,445 to 1,765 tokens.
• Fewer try-and-fix loops. One build task went from 18 model calls to 12.
• Start editing sooner. In a ReactBench task Particle-1 made its first patch on call 12 instead of call 27.

3. Particle-1 benchmarks

Across Terminal-Bench 2.1, ReactBench, ADE Bench and DeepSWE 1.1, Particle-1 used 28.4–52.2% fewer output tokens and cost 34.4–51.6% less per task than DeepSeek V4.1 Flash. Pass rates stayed inside the base model's confidence range.

General skills held up too: GPQA Diamond 90.40% (DeepSeek 90.91%), MMLU 86.08% (85.98%), Tau Airlines 86.00% (88.00%). On a legal contract-review test outside its training, Particle-1 used 24.6% fewer tokens and passed all eight reports, while the base model passed 62.5%.

4. Particle-1 price vs DeepSeek

The point is not a lower token price. Particle charges about what DeepSeek charges. You save because the model simply writes less. If your agent harness caches less than Particle's test setup, your savings may differ, so measure cost per task on your own jobs.

5. How to use Particle-1

1. Sign up at platform.particle.ai and create an API key (starts with pai-).
2. Point any OpenAI-compatible SDK at https://api.particle.ai/v1.
3. Set model to particle-1. Reasoning is on by default (high); set reasoning_effort to none to turn it off.
4. Send prompt_cache_key on long agent sessions to keep caching high.
5. Or call particle-ai/particle-1 on OpenRouter with the key you already have.

6. Availability and limits

Particle-1 is live now on the Particle API and OpenRouter, with one provider behind it. Early OpenRouter uptime has been shaky, so add a fallback model in production. Weights are not released. The first version is tuned for coding; Particle says other domains come next.

7. Where Sumus fits

Yes, you can use cheap, token-light models like Particle-1 and DeepSeek next to Claude and GPT. In Sumus, each model works as an AI employee in one team chat. Let a fast DeepSeek-class model draft the code, have Claude review the diff, and keep your team's sign-off in the same thread. You spend top-model tokens only where they matter.

What we're watching

• Independent token and accuracy tests from Artificial Analysis.
• Open weights or more providers for Particle-1.
• Particle models beyond coding.

Sources

• Particle, Introducing Particle-1, Oct 28, 2026.
• Particle API docs, docs.particle.ai, read Oct 30, 2026.
• OpenRouter, particle-ai/particle-1, read Oct 30, 2026.
• Suhail Doshi on X, Oct 28, 2026.