Particle launches Particle-1, saying that compared with the DeepSeek original, it reduces output by approximately 45%Output Token
BIBIBI
AT A GLANCE
Particle releases Particle-1, claiming it reduces average 45% output while maintaining a pass rate close to the original version Token。
Article
Particle-1 Release: Based on DeepSeek optimization, the task success rate is close to the original, while output Token is reduced by approximately 45%Beating AI breaking news: Mixpanel co-founder,AI founder of image platform Playground, Suhail Doshi, has started another venture, establishing a new company called Particle and launching its first model,Particle-1. The model is based on DeepSeek V4.1 Flash with reinforcement-learning post-training, primarily reducing the amount of Token consumed when a Coding Agent executes tasks.
Particle's four programming and data engineering benchmark results show that, compared with the original DeepSeek，Particle-1 output,Token is reduced by 28.4%～52.2%, averaging approximately 45%overall, while the task pass rate remains within the original model's statistical confidence interval. Based on the same Token unit price and a fixed cache hit rate, the cost per task is reduced by 34.4%～51.6%.
The saved Token primarily comes from more concise reasoning, fewer repeated attempts, and stopping promptly after task completion. In a Git deployment test, both models successfully completed the task, but Particle-1 output only 15,892 item Token, while the original used 47,006.Particle-1 is now available at API and integrated with Vercel AI Gateway. The current performance and cost data primarily comes from the team's own testing.
Original link https://m.theblockbeats.info/flash/374354
Key points
01
Particle-1 is based on DeepSeek V4.1 Flash and post-trained with reinforcement learning.
02
Across coding and data engineering evaluations, Particle-1 compared with the original version DeepSeek output Token reduced by 28.4%～52.2%, with an overall average of approximately 45%。
03
Particle stated that Particle-1's task pass rate remains within the original version's statistical confidence interval.
04
Using the same Token unit pricing and a fixed cache hit rate, the per-task cost decreased by 34.4%～51.6%。
05
During a Git deployment test,Particle-1 output 15,892 items Token, while the original version output 47,006 items Token, and both models completed the task.
06
Particle-1 is now available API, and integrated with Vercel AI Gateway。
07
Current performance and cost data primarily come from the team's internal testing.
AI-assisted interpretation
The following is analysis, separate from reported facts. Verify important claims independently.
Particle-1 is DeepSeek V4.1 a model further trained on the basis of Flash, aimed at enabling coding agents to produce less output when completing tasks Token. The article states that it reduced output volume in testing while maintaining a task pass rate close to that of the original version.
Why it matters to readers
If the team's self-test results can be independently replicated, the per-task cost of using coding agents may decline; however, actual results also depend on pricing, caching, and performance on real-world tasks.
Token can be understood as units used to bill for a model's processing and generation of text. Reduced output Token usually means that the billable usage consumed by a single task may decrease.
Risks and unknowns
The comparisons of performance, cost, and pass rates mainly come from Particle's internal testing; the independent review mentioned in the article has not yet occurred.
The article does not provide Particle-1 API pricing, availability by region, or terms of service.
The article does not specify Vercel AI Gateway's actual production availability status or model identifier.
The specific names and complete testing methodology of the evaluations are not provided in the evidence.
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