Vision-language compositionality is the ability to identify which properties, relations, quantities, and states bind to which entities in both an image and a caption, and to match those bindings across modalities. Existing benchmarks evaluate this ability through image-text matching: a model ranks candidate image-caption match similarities and succeeds when the correct match comes on top. In such datasets, paired images differ by more than a single binding: scene, layout, and content all differ, with 97% of pixels changing on average. Such pairs do not isolate compositionality: results are confounded by global image differences. We introduce Compositional Single-EdiT (CompoSET), a benchmark dataset in which each base image-caption pair has an edited counterpart, or foil, that preserves the scene but differs by a single controlled edit, such as color, count, spatial relation, or object state. Base and foil images preserve naturalistic scene complexity yet are visually near-identical: only 8% of pixels change on average. Our dataset has many foils per base scene (~22) in order to separate scene difficulty from edit difficulty, a distinction one-foil-per-base benchmarks cannot make. CompoSET contains 1,776 instances across 80 naturalistic scenes, spanning 16 edit types in 4 semantic categories, with captions at 3 verbosity levels. We test 22 dual-encoder VLMs and find that all models perform at or below chance level on CompoSET, despite scoring well on prior benchmarks. This suggests that apparent progress under prior image-text matching protocols may be an artifact arising from confounds in benchmark design, and that compositional understanding in dual-encoder VLMs is far from solved. CompoSET addresses the confounds and offers a better controlled benchmark to measure future progress.

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