Moonshot AI has acknowledged a problem with usage-limit deductions in the paid service for its latest artificial intelligence model, Kimi K3.1 Pro, and moved to compensate users. The company launched its top-tier model highlighting performance competitiveness, but a problem emerged in which usage was exhausted before real work was completed, bringing service stability and cost management to the fore as key tasks for commercialisation.

On Oct. 26, local time, blockchain media outlet Cryptopolitan reported that Moonshot AI decided to restore 25 percent of monthly usage limits on a one-off basis for all active users. Users whose remaining quota is below the restoration threshold will be compensated by resetting their usage counters.

Moonshot AI apologised on Oct. 25 in a notice for usage restrictions and unnecessary quota consumption. The company said the problem was its responsibility and should have been resolved earlier, and it would provide usage restoration for subscribers across all plans. It said the adjustments would be applied on the day of the notice.

The issue stemmed from paid users seeing their usage limits reduced unnecessarily even before completing actual tasks. Moonshot AI adjusted plan-related settings to reduce unnecessary sub-agent calls and to limit overly long responses. A sub-agent is an auxiliary system that takes on part of the work from a higher-level AI agent. Splitting work across multiple agents can help solve complex problems, but repeated calls can also increase token usage and costs.

Kimi K3.1 Pro is Moonshot AI's top-tier 3T-series model released on Oct. 23. It supports a context window of 1 million tokens and is available through kimi.ai, Kimi Work, Kimi Code and APIs, among others. The high-speed version operates about 2 to 3 times faster than the standard version. The full model weights are scheduled to be 공개 on Nov. 4.

Moonshot AI is also highlighting competitiveness in model performance. In the Terminal-Bench 4.0 evaluation released by the company, Kimi K3.1 Pro scored 65.7 percent. That is higher than GPT-6 Astra's 58.2 percent and close to the 66.4 percent recorded by Anthropic's Claude Opus 5.5. The score, however, is an evaluation result presented by the company, and needs to be distinguished from indicators that comprehensively show actual service quality and cost efficiency.

The episode has again highlighted operating costs and usage management issues for autonomous AI agents. When an agent delegates tasks to other agents or generates results through multiple steps, it can use more tokens than typical question-and-answer interactions. A study Anthropic 발표 in 2025 also found that using agents consumed more tokens than general conversational use, and that the increase was even larger in multi-agent systems.

Research to reduce related costs is also continuing. A 2026 study by Harbin Institute of Technology mentioned in the original text reported it reduced token-weighted placement costs by 85.2 percent versus a baseline by assigning relatively cheaper models to simple tasks. Moonshot AI's decision to reduce sub-agent calls and response length can also be seen as a step to control unnecessary token consumption arising in AI agent operations.

Moonshot AI also faced service capacity problems due to a surge in demand on July 19. At the time, the company said demand for Kimi K3 was putting pressure on GPU resources and temporarily suspended new subscriptions. It has not been confirmed whether there is a direct connection between the GPU capacity shortage then and the current quota deduction problem.

The industry views not only AI model performance but also stability and cost efficiency in actual use as factors that will determine competitiveness in paid services. In particular, for autonomous AI agents, predicting and controlling token usage becomes important as tasks become longer and steps more complex. Even if a model's performance is strong, the value users perceive is bound to fall if usage limits are exhausted before tasks are finished.

Attention is on whether the quota restoration and system adjustments will help restore user trust, and whether Moonshot AI can secure both performance and cost efficiency at the same time.