I built ModelScout to answer one question: which LLM should I actually use for my product?
Public benchmarks (MMLU, HumanEval, SWE-bench) measure general capability but don't predict performance on your specific tasks. I wanted something that would: upload your prompts, run them through multiple models, get quality scores with confidence intervals and significance tests.
The core eval uses 45+ metrics and LLM-as-a-judge with a structured rubric: each response is scored on delivery, constraint compliance, and equivalence to expected
output. Three judge tiers at different price/accuracy tradeoffs. Results include pairwise significance tests so you know when a quality difference is real vs noise.
To validate the approach, I built a dataset of 50 infrastructure tasks extracted from real open-source deployment repos on GitHub — specifically OpenClaw projects deploying AI tools to production with Kubernetes, Terraform, and Docker. The repos were created in 2026, so the code is unseen in any model's training data. Each task has a verified expected output from the actual repository.
Public benchmarks (MMLU, HumanEval, SWE-bench) measure general capability but don't predict performance on your specific tasks. I wanted something that would: upload your prompts, run them through multiple models, get quality scores with confidence intervals and significance tests.
The core eval uses 45+ metrics and LLM-as-a-judge with a structured rubric: each response is scored on delivery, constraint compliance, and equivalence to expected output. Three judge tiers at different price/accuracy tradeoffs. Results include pairwise significance tests so you know when a quality difference is real vs noise.
To validate the approach, I built a dataset of 50 infrastructure tasks extracted from real open-source deployment repos on GitHub — specifically OpenClaw projects deploying AI tools to production with Kubernetes, Terraform, and Docker. The repos were created in 2026, so the code is unseen in any model's training data. Each task has a verified expected output from the actual repository.
Full writeup: https://modelscout.co/blog/chatgpt-vs-claude-for-devops
https://modelscout.co