OpenAI Evals vs Unprompt: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of OpenAI Evals and Unprompt — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
OpenAI Evals
OpenAI
Open-source framework and registry for creating, running, and comparing evaluations of large language models and LLM systems.
Key features
- Registry of Benchmarks: A curated, open registry of existing evals and benchmarks for common LLM tasks, enabling quick comparison across models and tasks.
- Custom & Private Evals: Author and run custom evals using your own datasets and grading logic; private evals let teams evaluate proprietary workflows without exposing data publicly.
- Grader Framework: Build rubric-driven automated graders, model-based graders, or human-in-the-loop grading pipelines to produce consistent, repeatable scoring.
- CLI/SDK & API Integration: Python-first SDK and CLI that integrate with the OpenAI API, support threaded execution, detailed logs, and programmatic control for batch runs.
- Continuous Evaluation (CE): Integrate evals into development workflows to run on changes, detect regressions, and track performance over time across model versions.
- Detailed Reporting & Metrics: Produces sample-level logs, aggregated counts and metrics, and final reports that summarize correctness, rubric scores, and other custom metrics.
- Extensibility & Reproducibility: Templates and examples in the repository make it straightforward to extend eval types (e.g., classification, generation, instruction following) and reproduce results.
- License & Contribution Controls: Public contributions are MIT-licensed with clear expectations about contributor rights and OpenAI’s reserved rights to use contributed data for product improvements.
- Open-source registry of prebuilt evaluation suites (benchmarks) for LLMs
- Author and run custom evals and private evals using your own data
- Integration with OpenAI API and Evals API / dashboard for running and tracking evals
- Support for structured outputs and JSON schema-based graders
- Automated grader / LLM-as-judge capabilities to estimate human judgments
- CLI and Python-based tooling; examples and Jupyter notebook demos
- Threaded and batched execution for running large eval sets locally
- Support for continuous evaluation (CE) workflows and comparison across runs
- MIT-licensed contributions with requirement to have rights for uploaded data
- Logging and reporting features with summary counts and final reports
Best for
- Benchmarking Models: Run the registry or custom evals to compare multiple model families or model versions on shared task suites and metrics.
- Prompt Optimization: Use dataset-driven evals to measure the effect of prompt edits and automatically iterate toward higher-quality prompts.
- Continuous QA for Deployments: Integrate evals into CI/CD to run continuous evaluation that catches regressions when changing prompts, models, or system components.
- Private Workflow Validation: Create private evals using internal data to validate an LLM’s behavior on organization-specific tasks without sharing sensitive data publicly.
- Automated Grading & Labeling: Build automated graders and rubric pipelines to approximate expert judgments, triage outputs for human review, and scale label generation.
- Research & Method Development: Use the open registry and tooling to prototype new evaluation methodologies, reproducible benchmarks, and shareable tasks with the community.
- Comparative Performance Analysis: Track and report differences in accuracy, rubric scores, and failure modes across model releases for decision-making and model selection.
- Benchmarking and comparing LLM models on task-specific datasets
- Building private evaluation suites that reflect production workflows without exposing data
- Automated grading and preference estimation to approximate human ratings
- Continuous evaluation in CI to detect regressions and nondeterministic behavior
- Measuring model performance on real-world occupation or task benchmarks (e.g., GDPval)
- Developing and validating model improvements prior to deployment
Unprompt
Polymorph
iPhone and Mac app that measures your AI usage in tokens, API-rate cost and water, without judging it.
Key features
- Automatic Measurement: Mac reads local AI tool logs and iPhone uses Screen Time, so there is nothing to count manually.
- Evening Check-In: A one-tap daily confirmation of personal versus work AI use, with adjustments allowed.
- Pixel Calendar: Each day becomes a pixel colored against your own average, with streaks and a full-year band.
- Usage and Cost (Premium): Tokens, the API-rate cost and estimated water use over today, 30 days and full history.
- Codex Quotas and Widgets (Premium): Quota countdowns plus USAGE and LIMITS widgets.
- Milestones and Friends: 62 milestones and friend trends via QR code or link with no account needed.
- Private by Design: Data stays on device and in your iCloud, with no conversation read and no ads or tracking.
Best for
- Self-Awareness: See how much you rely on AI day to day without being blocked or lectured.
- Cost Tracking: Estimate what your token use would cost at API rates.
- Environmental Awareness: View an estimate of the water behind your AI usage.
- Work-Life Separation: Separate personal AI use from work-hours use.
- Quota Planning: Watch Codex limits and reset countdowns to avoid wasting capacity.
- Group Accountability: Compare trends with friends without sharing exact numbers.