If you’re building visual shopping, image or document understanding, or chart analysis, you need a way to verify whether your model’s response is actually grounded in the source image. A text-only evaluator cannot tell you whether a caption faithfully describes an image, whether an extracted invoice total matches the document, or whether a screen summary hallucinated a button that was never on the page. Gartner predicts that by 2030, 80% of enterprise software will be multimodal, up from less than 10% in 2024. Without automated multimodal evaluation, you’re stuck between expensive human review and unreliable text-only proxies.

Today, we’re announcing four new multimodal large language model (MLLM)-as-a-Judge evaluators for image-to-text tasks in Strands Evals software development kit (SDK): Overall Quality, Correctness, Faithfulness, and Instruction Following. Each evaluator scores image-to-text outputs against the source image. The evaluator sends the image directly to a multimodal judge model, alongside the query, the response, and (optionally) a reference answer. The judge returns a score grounded in the image, together with a reasoning string you can use for debugging. You can use these evaluators as drop-in replacements for text-only judges in your existing Strands Evals Case → Experiment → Report workflow, and plug them into continuous integration (CI) to catch visual hallucinations, factual errors, and instruction violations automatically.

Figure 1: Overview of the multimodal judge framework. Given an image (or document image), a textual query, and a model-generated response, the framework constructs a multimodal evaluation prompt, applies an MLLM-based judge, and returns a score (Likert 1-5 or binary) along with reasoning. The framework supports both reference-based and reference-free evaluation, and integrates with Strands Evals for case management and reporting.

Suppose you’ve shipped a model that reads invoices, summarizes dashboards, or narrates screenshots. Running a text-only LLM-as-a-Judge over the response gets you some signal (the writing is fluent, the structure is clean), but it misses exactly the failures that matter:

A text-only judge reads the output and approves it without verifying the image. The ground truth lives in the image, and the judge never sees it.

Even when you do get a low score from a holistic “rate overall quality” judge, the score alone doesn’t tell you what broke. The failure could be a factual error, an invented detail, or an ignored instruction. These three failure modes require three different fixes, so collapsing them into one score makes debugging harder than it needs to be.