Model-based robotic grasping estimates an object's six-dimensional (6D) pose and plans on its mesh as if that pose were exact. We show that this assumption reduces simulated grasp success even when the pose passes the standard accuracy test. We built LM-Grasp, a benchmark of 15{,}800 real LINEMOD RGB-D images with grasp outcomes simulated on scanned meshes. FoundationPose meets the ADD(-S) criterion (mean model-point error below 10\% of the object diameter) on 0.997 of test scenes, yet planning on its estimate succeeds in 0.791 of scenes, compared with 0.856 using the annotated pose. Because the estimator reports no uncertainty, we build a pose posterior from its own errors on annotated training frames, resampled from frames with similar observable fit, and score each grasp by its mean success across sampled poses and four equally weighted physics scenarios. Without retraining the estimator, posterior planning yields 0.859 (difference from the annotated-pose result +0.003; 95\% scene-wise paired-bootstrap interval [$-$0.003, +0.009]). This interval is compatible with no difference and a small shortfall; we did not conduct an equivalence test. For pose errors of 5 to 10\% of the diameter, simulated success rises from 0.796 to 0.984. Among test scenes assigned singleton-success conformal prediction sets, observed held-out precision is 0.972, compared with 0.904 for the point-estimate planner; these are empirical precision estimates, not conditional guarantees. On occluded LM-O scenes never used for tuning, posterior planning closes 66\% of the gap to the annotated pose.

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