Which AI model can you actually trust?
ModelTrust is an AI model evaluation platform that runs the same questions across GPT-4, Claude, Gemini, and other models simultaneously. It uses structured benchmarks (Likert scales, binary choices, and forced comparisons) to measure reliability, detect disagreement, and track cost per query, so organizations can make evidence-based decisions about which AI to deploy.
The Problem with Trusting AI
Every organization using AI faces the same question: which model gives the right answer? Here are the concepts behind how ModelTrust helps you find out.
- AI Model Evaluation
- The systematic process of testing language models against defined questions to measure accuracy, consistency, and reliability. Rather than relying on generic benchmarks, model evaluation tests AI against your specific use cases and criteria.
- Trust Scoring
- A quantified reliability metric calculated from a model's performance across an evaluation. Trust scores factor in response consistency, output validity, calibration accuracy, and agreement with other models to produce a single number that represents how much you can rely on a model's outputs.
- AI Model Reliability
- The degree to which an AI model produces consistent, well-formed, and accurate outputs across repeated queries. Reliable models give similar answers to similar questions, format their outputs correctly, and avoid hallucination. Unreliable models produce inconsistent or contradictory responses that require constant human verification.
- Model Agreement and Disagreement
- When multiple AI models are asked the same question, agreement means they converge on the same answer. Disagreement means their outputs diverge. High disagreement on a question is a signal that the answer is uncertain and may need human review. ModelTrust measures this automatically for every question in an evaluation.
- Benchmark Evaluation
- A structured evaluation using standardized question types (Likert scales, binary choices, forced comparisons, numeric scales) that produce quantifiable, comparable results. Benchmark evaluations let you measure model performance with statistical rigor rather than subjective assessment.
- Human Review Signals
- Automatic flags that indicate when AI outputs should not be trusted without human verification. ModelTrust generates these signals when models disagree, when confidence is low, or when responses contain patterns associated with unreliable outputs. The goal is to focus human attention where it matters most.
Why Not Just Ask ChatGPT?
Ad hoc testing means asking a model a few questions and eyeballing the answers. It feels productive, but it tells you almost nothing. You have no baseline, no comparison, and no way to know if the answer you got today will be the answer you get tomorrow.
Structured evaluation is different. You define specific questions, run them across multiple models simultaneously, and measure the results quantitatively. When three models agree on an answer and one disagrees, that disagreement is a data point. When a model scores 92% reliability on your evaluation but only 64% on another, you know exactly where to trust it and where not to.
ModelTrust exists because choosing an AI model for production should be based on evidence, not gut feeling. Generic leaderboard benchmarks test general knowledge. Your business needs are specific. ModelTrust lets you test what actually matters to you.
Features
Multi-Model Evaluation
Run the same questions across GPT-4, Claude, Gemini, and others. See how each model handles your specific use case.
Benchmark Question Types
Structured evaluations with Likert scales, binary choices, forced comparisons, and more. Not just vibes, real data.
Cost & Token Tracking
See exactly what each model costs per question. Compare quality against price to find the best value.
Side-by-Side Comparison
Put model outputs next to each other. Spot differences, measure divergence, and identify which models agree.
How It Works
Create an Evaluation
Define the questions you want to test. Pick from structured question types or write open-ended prompts.
Select Your Models
Choose which AI models to evaluate. Run them all against the same questions simultaneously.
Analyze the Results
Compare outputs, review reliability scores, and identify where models disagree. Know when to trust the answer.
Frequently Asked Questions
What is ModelTrust?
ModelTrust is an AI model evaluation platform that lets you run structured questions across multiple language models, compare their outputs, and measure reliability. It helps teams decide which model to trust for specific use cases.
How does ModelTrust compare models?
You create an evaluation with questions, select the models you want to test, and run them all simultaneously. ModelTrust collects responses, calculates agreement scores, flags disagreements, and identifies when outputs need human review.
What models does ModelTrust support?
ModelTrust supports OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), and xAI (Grok). New providers can be added through the adapter system.
What is AI model evaluation?
AI model evaluation is the process of systematically testing language models against defined questions to measure accuracy, consistency, and reliability. Instead of relying on general benchmarks, ModelTrust lets you test models against your own questions and criteria.
What is a trust score?
A trust score is a quantified reliability metric calculated from a model's performance across an evaluation. It factors in response consistency, JSON validity rates, calibration accuracy, and agreement with other models. Higher scores indicate more reliable outputs for your specific use case.
How much does ModelTrust cost?
ModelTrust is currently in private beta and free to use during the beta period. You only pay for the API costs of the models you evaluate (using your own API keys). Pricing for the hosted service will be announced when we launch publicly.
Who built ModelTrust?
ModelTrust is built by Idea Warehouse, a software company founded by Colin Smillie. Colin is a software engineer and entrepreneur focused on building tools that help teams make better decisions with AI.
Get Early Access
ModelTrust is in private beta. Sign up to be among the first to evaluate AI models with confidence.