Particle-1 Comes to OpenClaw: 45% Fewer Output Tokens, but Long Tasks Still Have No Answer

In OpenClaw's v2026.10.10 update, Particle-1 is already available. The key figures from the Particle API are: about 45% fewer output tokens than DeepSeek V4.1 Flash, comparable benchmark accuracy; zero data retention for standard inference. If you want to try it, update OpenClaw to v2026.10.10. Particle's introduction is here.

A 45% difference in output tokens is very direct for applications billed by token. With shorter output, generation-side cost and latency both have a chance to drop. The official claim is comparable benchmark accuracy; the specific scores and test conditions are in the Particle blog, so do not look only at a single percentage. Zero data retention for standard inference is a plus for products that care about data retention, but the details of the terms still need to be checked in the provider's documentation.

In the replies, some people are preparing to update that night. Others ask more practically: does the token saving still hold for long agent runs and large numbers of tool calls, or is it only obvious on single-turn benchmarks? This OpenClaw tweet does not answer that. For teams that need to run long chains, this question is more critical than the 45%. The output distribution of single-turn benchmarks differs from that of agent tool calls, so the savings ratio cannot be directly extrapolated. A safer test is to take one of your own long task chains, record output tokens, tool-call rounds, and task completion rate, then compare with DeepSeek V4.1 Flash. Do not test only a single Q&A.

What OpenClaw users can do now: update to v2026.10.10 and try Particle-1 through the existing model entry point. Ordinary users can see this as a model-option update; developers should care more about billing and stability on long tasks. The official data provides a starting point, but the community's question still has no answer.