Real‑Time AI Benchmarking & Model Trading Platforms cover a new class of services that combine continuous performance measurement with marketplace mechanics to let organizations discover, compare, buy, route to, and deploy models in production. As of 2025‑12‑10 this space is driven by demand for transparent, reproducible metrics (latency, throughput, prompt‑level accuracy, robustness, safety), dynamic pricing and versioning, and integrations into cloud ML stacks and game‑style testbeds for stress testing. Platforms such as Gensyn Delphi and its competitors surface live evaluation data and historical telemetry alongside commercial terms so operators can select models based on current performance and cost. They sit at the intersection of AI tool marketplaces (for model discovery and transactions), market intelligence tools (for price discovery, usage analytics, and regulatory provenance), and game AI engines (as repeatable environments for adversarial, multi‑agent, and latency‑sensitive benchmarks). Key ecosystem players integrate differently: Vertex AI and Google Gemini provide end‑to‑end model development, hosting and multimodal inference APIs suitable for pipeline orchestration; Mistral AI supplies efficient open models and enterprise production tooling emphasizing privacy and governance; Cohere focuses on private customizable LLMs, embeddings and retrieval; IBM watsonx Assistant targets no‑code and developer-driven virtual agents and orchestration. Model trading platforms rely on these providers for supply, runtime, or baseline comparisons. Adoption considerations include standardized benchmarking protocols, provenance and compliance records, cost and routing policies, latency vs. quality tradeoffs, and the operational complexity of multi‑model routing. For enterprises and developers, these platforms promise more objective model selection, but require careful governance to manage risk, reproducibility, and vendor lock‑in.