Define the modality and the job
Two products may both accept images while solving different problems. One may read documents, another may understand photographs, and a third may follow a live screen. Start with the actual input—scanned invoices, charts, UI screenshots, calls or video—and the decision the model must support.
Then separate extraction from interpretation. Ask the system to transcribe visible values before asking it to explain a chart. If the conclusion is wrong, this reveals whether the failure began in perception or reasoning. Without that separation, a fluent answer can hide a basic reading error.
Build a representative test set
Use clean and messy examples: rotated pages, small text, unusual accents, background noise, long clips and conflicting cues. Include cases where the correct response is uncertainty. Score exact fields when possible and use a written rubric for qualitative judgments.
Latency and cost belong in the same test. Video frames and long audio can consume far more context than text, while real-time voice requires stable response timing. A model that wins on a single hard image may still be the wrong system for thousands of routine documents or an interactive call.
Test the workflow, not only the model
Production quality depends on preprocessing, file limits, tool calls and the interface used for correction. A document system may need OCR fallback; a voice system needs interruption handling; a visual agent needs permission boundaries before it can click anything.
Record the model version, prompt, input file and output for every evaluation. Re-run the same set after major updates. Multimodal systems change quickly, and a durable test harness is more valuable than a comparison article that freezes one moment in time.
Sources & further reading
Social-media activity is treated as a signal of attention, not proof. Product claims are attributed to the linked publisher or announcement.