Moonshot AI sets $2 billion annualized revenue goal for Kimi

Moonshot AI, the Chinese lab behind Kimi, is targeting $2 billion in annualized revenue by the end of 2026, Bloomberg reported. The objective is double the revenue run rate the company reportedly reached in August and follows the summer release of its K3 open-weight model.

The target would represent meaningful commercial progress for a company whose model weights are freely available. Moonshot’s outlook remains far below reported revenue figures for closed-model competitors: recent reports put OpenAI at $40 billion and Anthropic at $65 billion.

K3 usage supports the commercial ambition

Moonshot links the growth outlook to the reception for K3. Although usage has declined slightly in recent months, OpenRouter data shows that K3 models generate as many as 300 billion tokens per day on its system. That level of activity offers a visible measure of continued demand for the models.

The company’s position illustrates a central trade-off in the AI market. Freely available weights can support wide use of a model, while closed-weight providers retain a different economic structure. Moonshot consequently has far lower margins than its closed-weight competitors, even as its revenue projection indicates that open-weight models can still produce substantial sales.

Training allegations add a material question

The revenue goal comes days after Anthropic accused Moonshot of a long-running model-distillation campaign. Anthropic alleged that nearly 300,000 requests from Kimi were routed directly to Claude Opus, effectively serving Opus instead of Moonshot’s own models.

Anthropic further alleged that more than 23 million responses from its models were collected for Moonshot’s training. The allegation is separate from Moonshot’s revenue target, but it places the company’s model-development practices under heightened scrutiny.

What businesses should evaluate

For customers and partners, the story is not only about token volumes or a headline revenue goal. The company’s earlier financing, reflected in Moonshot AI funding and open-weight LLM demand, also points to the market interest surrounding open-weight large language models in China.

Businesses evaluating AI suppliers should weigh the availability and usage of a model against its commercial model, margins and the questions surrounding its development practices before making a dependency decision.