Gunjo · Business Intelligence for the AI Era
← Sticker Wall MODEL · DETAIL

Revenue-Sharing Clauses for Open-Source LLMs: The Freemium Model of Qwen3.8-Max and Kimi K3

1) Primarily derived from revenue sharing, taking a fixed percentage of commercial income exceeding the threshold (e.g.,

MODEL

Key Fields

FIELD STAMPS
IndustryAI / LLM
RegionChina
ScaleGiant
ChannelOnline

📌 Background

As the competition for open-source LLMs intensified in 2026, the high cost of computing power became difficult to cover through pure free open-source models, leading leading vendors to introduce revenue-sharing clauses. Alibaba Cloud's Qwen3.8-Max and Moonshot AI's Kimi K3, while open-sourcing their model weights, now require a percentage of revenue once commercial earnings exceed a certain threshold (e.g., $20 million), marking a shift from free to a freemium model for open-source LLMs.

👤 Target Customers

Enterprise clients deploying open-source LLMs commercially, including cloud providers, SaaS companies, and AI application developers; the payers are commercial users whose revenue hits the sharing threshold.

💰 Revenue Streams

1) Primarily derived from revenue sharing, taking a fixed percentage of commercial income exceeding the threshold (e.g., $20 million); 2) Incremental revenue through bundled cloud API calls and enterprise support services; 3) Threshold-based surcharges: a percentage of commercial revenue above the threshold, with cloud API calls and enterprise support billed separately based on usage.

🧮 Cost Structure

Model training compute costs, open-source community maintenance costs, legal and revenue-sharing accounting costs, and cloud infrastructure costs.

🛡️ Moat

First-mover advantage in model weights and ecosystem, creating a closed loop of open-source traffic acquisition and cloud monetization, with revenue-sharing clauses built upon the scarcity of LLM capabilities and brand trust.

🔑 Keys to Success

  • Precisely design revenue-sharing thresholds and percentages to balance adoption rates with monetization
  • Provide supporting cloud services and toolchains to naturally convert open-source users into paying cloud customers

⚠️ Risks

  • Enterprises bypassing revenue-sharing oversight through private deployment
  • Open-source community protests against commercial clauses leading to ecosystem fragmentation
  • Regulatory constraints on technology exports and revenue-sharing settlements

🏢 Cases

  • Alibaba Cloud Qwen3.8-Max launches a revenue-sharing program with a $20 million threshold
  • Moonshot AI Kimi K3 introduces revenue-sharing clauses

📊 SWOT Analysis

Strengths

  • Lowers the barrier to entry for enterprises, rapidly expanding the developer ecosystem and influence
  • Provides a capital recovery channel for continuous model iteration

Weaknesses

  • Revenue-sharing clauses may limit deep enterprise adoption, forcing some users to switch to completely free models
  • Poorly designed thresholds and percentages can easily trigger community backlash

Opportunities

  • Strong demand for enterprise-grade AI commercialization, allowing for bundling with cloud services and toolchain consumption
  • Open-source model attracts global developers, facilitating expansion into overseas markets

Threats

  • Substitution by other completely free or more open-source models
  • Closed-source LLMs capturing commercial clients through superior performance