Particle-1, launched by the Particle Team, reduces output tokens by 45%.

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TOKEN
$0.001689-0.7%
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GLM
$0.11292.63%
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BASED
$0.049312.94%
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On-chain news: The Particle team has launched Particle-1, a model based on DeepSeek V4.1 Flash, designed to enhance token efficiency. On software engineering tasks, Particle-1 reduces output tokens by 45%, with an average cost of $0.2211 per task—lower than DeepSeek V4.1 Flash ($0.3368) and GLM 5.3 Flash ($0.6523). OpenClaw users must update to v2026.10.10 to access the model, which supports standard inference and zero data retention. Additional token listings may follow as adoption increases.
ME AI message: The Particle team has launched Particle-1, fine-tuned on DeepSeek V4.1 Flash, with the core goal of improving token efficiency. On software engineering tasks, the model reduces average output tokens by approximately 45%, with a cost of $0.2211 per task—lower than DeepSeek V4.1 Flash at $0.3368 and GLM 5.3 Flash at $0.6523; the average output tokens for the three models are 59,580, 83,246, and 98,155 respectively, with task pass rates of 72.57%, 71.15%, and 66.82%. On Terminal-Bench 2.1, ReactBench, ADE Bench, and DeepSWE 1.1, Particle-1 uses 28.4%–52.2% fewer output tokens than the base model, with Pass@1 within the base model’s confidence interval. Training employed Cascade RL, rank-64 LoRA (accounting for ~1.25% of parameters), GSPO, and the Miles framework. OpenClaw users must update to v2026.10.10 to use it; standard inference retains zero data. (Source: InFoQ)
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