Prevailing benchmarks reward raw capability and scale, creating a systematic blind spot where smaller, cost-efficient, task-specialized models that outperform large general models in actual deployment scenarios remain invisible to the metrics that drive funding and development priorities. There is no standardized evaluation layer that captures cost-per-correct-action, latency, or task-specific accuracy under realistic agent workloads. This misalignment between benchmark incentives and deployment reality causes the ecosystem to systematically over-invest in the wrong architecture class.

Current benchmarks ignore cost, latency, and task-specific accuracy, making it impossible to discover that a $0.002/call specialist agent outperforms GPT-4 on your actual workload — so teams overspend on bloated general models.

AI engineering leads at startups and mid-market companies deploying agents in production who need to justify model selection decisions with real economics, not vibes.

Teams already spend weeks running ad-hoc evals before every model switch; a shared, standardized platform with cost-normalized leaderboards collapses that to minutes and creates a discovery channel for specialist model providers hungry for distribution.

MVP: open-source eval harness that runs standardized task suites (tool-use, RAG, code-gen, data extraction) against any model API, tracking cost-per-correct-action, p95 latency, and accuracy — results auto-publish to a public leaderboard site; key tech is deterministic replay of recorded agent traces as the eval substrate.

Adjacent to the $2B+ MLOps/eval market (Weights & Biases, Braintrust, Arize); specifically targets the ~50K teams actively deploying LLM agents who each spend $10K-$500K/yr on inference.