Baidu Launches Ciyuan Exchange to Establish Trust Standards for AI Model Trading
Over the past three years the AI industry has focused intensely on building ever-larger models, yet enterprises still face significant friction when trying to procure and use those models reliably. Different platforms charge widely varying prices for the same model, while response speed, stability, and actual delivered performance can differ dramatically even when the underlying model is identical.
On 12 November at the Baidu World 2026 Conference, Baidu Intelligent Cloud officially launched the Ciyuan Exchange, a dedicated large-model trading platform. Within its first month the platform onboarded more than 100 supply resources, recorded over 50 million daily calls, and processed more than 700 billion tokens per day.
Li Yanhong, founder of Baidu, stated that the exchange is intended to function as a true marketplace for model tokens, allowing enterprises and individual developers to discover, purchase, and consume trusted model resources with clear quality standards and transaction rules.
The core challenge the platform targets is the asymmetry of information: suppliers know far more about real service quality than buyers. A China Academy of Information and Communications Technology report from October 2026 showed that the same open-source model can exhibit nearly 2× differences in tokens-per-second output depending on the hosting provider.
To reduce these risks, Ciyuan Exchange introduces two foundational capabilities: model verification and supplier credit scoring. Verification includes qualification checks, model fingerprint comparison, capability testing, performance benchmarking, and availability validation. Credit scoring relies on regular sampling, active probing, and full-link monitoring across service quality, commitment fulfillment, security compliance, and commercial performance.
While platforms such as OpenRouter already offer multi-model routing based on price and latency, Ciyuan Exchange differentiates itself by creating persistent, queryable credit histories that can become long-term competitive assets for high-quality providers.
The exchange also promotes a more sophisticated view of cost: rather than comparing price per million tokens alone, buyers can evaluate total cost of completing a task, factoring in retries, latency, accuracy, and business impact. This approach aligns with the maturing AI market’s need for reliable service-level guarantees rather than raw model power.
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