A novel approach to the track-to-track fusion (T2TF) of state estimates from interacting multiple-model (IMM) estimators using inside information [mode-conditioned estimates (MCEs) and mode probabilities] is described in this paper. Fusion is performed on-demand, i.e., without conditioning on past track data. The local trackers run IMM estimators to track a maneuvering target with switching process noise and they transmit MCEs and mode probabilities to a fusion center. The fused state posterior probability density is a Gaussian mixture, where the parameters of the required likelihood functions can be computed recursively. Mode probabilities are fused by transforming them to log-ratios and using them as statistical information in the likelihood function of the mode. This results in consistent data fusion based on known target and local tracker (IMM) parameters. Simulations show that this method outperforms the fusion of the local IMM estimator Gaussian-approximated outputs both in terms of error during target maneuvers and in terms of the consistency of the mean-squared error (MSE). It is a generalization of Gaussian T2TF with crosscovariance, and its performance is close to that of centralized measurement fusion (CMF)— by accounting for the error and log-ratio crosscovariances, the fused covariance consistency matches the ideal consistency of CMF without requiring memory of past fused tracks. The method is also shown to be more accurate, informative, consistent in MSE, and of lower computational and communication cost than Chernoff fusion, a recently published method for Gaussian mixture fusion.