PromptEval vs PromptPex

PromptEval is a web and API SaaS platform for evaluating and optimizing large-language-model prompts. It assigns a reproducible score from 0 to 100, with diagnostics covering clarity, specificity, structure and robustness, then identifies issues, warnings, strengths and targeted fixes. A token optimizer compresses prompts while preserving intent and reports the reduction percentage. Its versioned library keeps prompt history, score changes and diffs. The A/B Playground compares two prompts across as many as seven criteria using a user-provided API key. An official GitHub Action can fail pull requests after score drops, contradictions or production regressions. Pro and Team users can serve prompts by slug through an API endpoint, with changes taking effect in about 60 seconds. Plans include a free tier, Basic at $9 a month, Pro at $19 a month, and custom-priced Team.

PromptPex is an open-source tool for developers who keep AI prompts in their codebase and want to examine how models follow prompt requirements. It treats prompts as functions, extracts output rules expressed in natural language, and generates unit tests to check whether a model follows those rules. It can also evaluate prompt performance across models, using an LLM to judge whether outputs meet stated requirements. Test generation can be shaped with scenarios, expansions, custom instructions, and effort levels. The CLI accepts Markdown or text templates, Prompty files, and GitHub Models prompt YAML files. Setup lists OpenAI, Azure OpenAI, GitHub Models, and Ollama as supported providers. PromptPex is packaged as an npm CLI and can run in Docker; its setup guide also describes a local web interface. Documented integrations include OpenAI Evals, Azure OpenAI Stored Completions, GitHub Models Evals, and GitHub Actions. Microsoft is identified as the maker. The project is intended for research, and its maker advises against downstream use without additional evaluation of robustness, safety, harm, and bias. Vague prompts can lead to suboptimal results, and rule extraction may be less accurate with complex or idiomatic language.