Kimi K3.1 Pro Apologizes Within 48 Hours of Launch and Compensates Members with 25% Quota

[[**Kimi K3.1 Pro Apologizes 48 Hours After Launch for Rate Limiting and Abnormal Quota Consumption, Compensates Subscribers with 25% Monthly Usage; Incident Reflects How Agent Tasks Drive Up Token Costs, Putting Independent Large-Model Vendors Under Pressure.**

**Key Points**

**1. Apology and Compensation Just 48 Hours After Launch**

On October 26, Kimi apologized for rate limiting and abnormal quota consumption on K3.1 Pro; the model's API pricing is only one-fifth that of comparable products, and subscribers receive a 25% monthly compensation, with usage below 25% reset to 0.

**2. Users Feel Quotas Run Out Faster**

A user on the 199-yuan tier used 70% of the 5-hour limit just installing one Skill; one hour of normal use consumed 27% of the 5-hour rolling quota; an Agent Swarm exhausted the five-hour window in about one minute, though this was an isolated case.

**3. Agents Drive Up Token Costs, Monthly Plans Under Pressure**

A single agent task consumes 10 to 100 times the tokens of an ordinary conversation; domestic daily calls are projected to reach 500 trillion by June 2026. Experts expect a shift toward basic monthly plans plus usage-based billing for advanced tasks.

**4. Compute Constrained; Open Source and Model Routing Become Cost-Reduction Directions**

This article is from the WeChat public account: Science and Technology Innovation Board Daily, author: Li Mingming

Agent tasks are getting heavier and heavier—how can large-model vendors find a balance between cost and experience?

In the early hours of October 26, the Kimi intelligent assistant officially issued an apology notice, publicly responding to issues such as rate limiting and abnormal quota consumption encountered by subscribers after the launch of its new model K3.1 Pro, and simultaneously rolled out a compensation plan.

The above incident came just 48 hours after the official release of this 3-trillion-parameter open-source model.

Just two days earlier, in the early hours of October 24, Moonshot AI had just launched its new-generation open-source large model Kimi K3.1 Pro. With a 3-trillion-parameter scale and a 1-million-token context window, the model maintains its position as the world's largest open-source model by parameter count, approaching the level of leading overseas closed-source models in multiple authoritative benchmarks, and its officially announced API pricing is only one-fifth that of comparable flagship products.

In the apology notice, the company admitted that "the problem is on us, and we should have fixed it earlier." According to the compensation plan, all subscribers within the validity period will receive a usage compensation of 25% of the monthly total; if a user's current used quota is less than 25% of the monthly total, the usage will be directly reset to 0, and the quota progress bar will be adjusted within the same day.

From attracting attention at launch to apologizing and compensating quotas, the twists and turns K3.1 Pro has experienced over these two days point to a more practical question: as Agent tasks get heavier and heavier, how can large-model vendors find a balance between cost and experience?

Unit Prices Keep Falling—So Why Do Quotas Run Out Faster?

Many paying users' intuitive feeling is that the new model's capabilities have improved, but the quota no longer lasts. Real feedback from community users concretely illustrates this perceived gap.

A Science and Technology Innovation Board Daily reporter saw in relevant communities that a user subscribed to the 199-yuan tier said they recognized K3.1 Pro's capability performance, calling it the top domestic model visibly available today, but that "quota anxiety" was very obvious during use. Merely having the model complete the simple task of installing a Skill consumed 70% of the 5-hour limit. This also makes users afraid to use it for light daily work, lest the quota run out accidentally.

Another user calculated that just one hour of normal use consumed 27% of the 5-hour rolling quota, accounting for 1.07% of the monthly total quota.

In heavier scenarios such as multi-step Agent tasks, complex code debugging, and multimedia generation, another user reported that after launching an Agent Swarm task to generate 100 gallery works, the five-hour window quota was exhausted in about one minute, the task produced no results, and the terminal kept popping up HTTP 429 errors. However, this situation is a single-user case and does not represent the general experience.

The contrast thus arises: the official side simultaneously lowered the per-token API pricing, but in terms of actual user perception on the consumer side, users vaguely feel it has become "more expensive."

A report previously released by the China Academy of Information and Communications Technology, the "Token Economy Development Research Report (2026)," provided an industry-level explanation. The token consumption of a single agent task can reach 10 to 100 times that of ordinary conversational applications; a single 1.5-minute AI short drama can consume 5 million to 10 million tokens. Domestic daily average token calls grew from 100 billion in early 2024 to 500 trillion by June 2026, an increase of more than a thousandfold. User demand has long shifted from simple Q&A to engineering-level complex tasks.

Zhu Keli, founding president of the Guoyan New Economy Research Institute and chief expert on the intelligent economy, told a Science and Technology Innovation Board Daily reporter that this contrast of "lower per-token prices but higher overall usage costs" is a structural industry change brought about by the deployment of Agent intelligences and will be a phased norm for some time to come.

In the past, under ordinary conversation mode, interactions were brief and consumption controllable, and the per-token price was basically equivalent to usage cost; entering the Agent era of long chains of thought and multi-tool calls, models must autonomously break down tasks and repeatedly verify and iterate, so the consumption of a single complete task surges exponentially. Even if unit prices are lowered, the extra consumption brought by redundant reasoning will still push up overall costs.

Regarding the direction of business models, Zhu Keli judges that the traditional C-end monthly subscription model with no upper limit is no longer sustainable, and the industry is moving away from the era of crude low pricing. In the future, the mainstream will shift to a tiered system of "basic monthly subscription as a floor + high-tier tasks billed by usage," with light daily interactions retaining affordable subscriptions and complex Agent workflows priced separately, achieving a two-way balance between user experience and enterprise costs.

Without a proprietary cloud foundation, open source is not a universal solution for compute power

Behind the apology announcement is the compute supply challenge commonly faced by independent large-model vendors. This is not the first time Moonshot AI has faced supply-demand fluctuations; after the release of K3 in July this year, user request volume exceeded expectations in a short period, and the platform also temporarily suspended new user subscriptions.

In contrast, leading overseas vendors: OpenAI is tied to Microsoft's Azure supercomputing cluster, and recently its AI models will connect to Google Cloud, further reducing its dependence on Microsoft; Anthropic relies on Google Cloud infrastructure and others, all backed by stable cloud compute foundations. Most domestic independent AI vendors do not have their own cloud ecosystems, and combined with external constraints on high-end chip supply, the total pool of available compute is relatively fixed, so they must always make dynamic allocations between next-generation model training and online inference services. The stronger the model capabilities, the more heavy tasks they attract, and the greater the pressure on the inference side; technological breakthroughs instead bring new resource tests.

The full weights of K3.1 Pro will be opened before November 4. Open source is seen as an important means of diverting compute pressure and expanding the developer ecosystem: after the weights are opened, a large number of enterprise-level inference needs can be deployed on their own clusters, alleviating the original vendor's load to a certain extent.

"The most core breakthrough point is not simply 'compensating for hardware with software,' but pushing the marginal production cost of Tokens to the limit in engineering architecture," Zhang Youyu, a distinguished researcher at Peking University, told a reporter from the Science and Technology Innovation Board Daily. Optimizing the attention mechanism and compressing KV cache to improve single-card throughput are only basic optimizations; the real key lies in Model Routing: using lightweight models to handle 80% of routine requests, tilting high-end compute toward complex long-chain Agent tasks, and relying on scheduling to reduce overall inference costs.

He believes that open source can shift inference loads outward and ease the original vendor's compute pressure, but it will also impact the original commercialization path. In his vision, the long-term route for independent vendors can be to "rely on open source to seize distribution discourse power over Token consumption, and rely on closed-source high-tier agents and vertical scenario solutions to obtain value-added revenue."

As of press time, the user quota adjustments on the Kimi platform have been implemented one after another, and optimizations for sub-Agent calls and output length are also underway. Compensation and temporary tuning can quickly respond to user demands, but the cost-effectiveness (i.e., Cost-Effectiveness ratio) question in the Agent era is far from being answered by a single product iteration.

From "building strong models" to "delivering a good experience," from "competing on technical parameters" to "calculating comprehensive costs," domestic large-model vendors still have a series of hurdles to clear, including compute scheduling, product design, and business models. AI industry signal channel: Frontier Technology. This content comes from the internet, original link; the views represent only the author and do not represent Huxiu's position. If there is any copyright issue, please contact hezuo@huxiu.com, and we will verify and handle it promptly. Everyone who is changing and wants to change the world is on the Huxiu APP[ ]