Multi-Model Testing

Multi-model testing compares the same prompt across models, providers, or parameters. It is useful for prompt tuning and model selection, not for judging quality from one lucky response.

When to Use It

• Compare configured models on the same task.
• Check whether a prompt only works with one provider or model family.
• Observe stability after changing temperature, context, or format constraints.
• Balance response quality, latency, and cost for a real workflow.

Basic Workflow

1. Configure at least one working model provider in Settings.
2. Open a prompt and fill its variables with fixed test data.
3. Select the models available for comparison.
4. Run the test.
5. Compare outputs against the same evaluation criteria.

What to Evaluate

Metric	Meaning
Correctness	Completes the task without material factual errors
Stability	Stays on track across repeated runs
Format following	Respects JSON, table, or step constraints
Cost	Fits the expected usage budget
Latency	Fits the real workflow

Tuning Advice

• Keep variable inputs fixed while comparing models.
• Change one factor at a time: prompt text, model, or parameters.
• Add explicit schemas or examples for structured output.
• Keep human review for high-impact decisions.

Privacy Boundary

Testing sends prompt content and filled variables to the providers you select. Remove sensitive data first, or use a trusted private or local deployment where appropriate.

Continue with AI Model Settings and Prompt Management.