Scaling up ranking models for industrial recommender systems faces two critical challenges: (C1) existing sequence tokenization fails to jointly capture context-aware and context-invariant user intent from heterogeneous behavior sources, and (C2) prevailing interaction mechanisms are both computationally expensive and semantically homogeneous, limiting prediction quality under strict online latency constraints. We propose \textbf{HeMix}, a scalable ranking model that unifies query-mixed sequence tokenization with heterogeneous feature interaction. To address (C1), HeMix introduces a \textit{Query-Mixed Interest Extraction} module that employs dynamic and fixed queries to simultaneously model context-aware and context-invariant interests from global and real-time behavior sequences. To address (C2), we design the \textit{HeteroMixer} block, comprising Multi-Head Token Fusion, Heterogeneous Mixed-Token Interaction and Group-Aligned Reconstruction, as an efficient alternative to self-attention that enables multi-granularity cross-feature modeling at linear cost. Crucially, HeMix scales smoothly from ${\sim}100$M to ${\sim}1500$M parameters by independently expanding block depth and token dimension, yielding steady accuracy gains without architectural redesign. Experiments on industrial-scale data show that HeMix achieves $+1.64\%$ relative CTR-AUC over the DLRM baseline at the ${\sim}100$M scale while requiring fewer GFLOPs than the strongest competitor. Deployed on the AMAP APP, HeMix yields +0.88\% GMV, +2.74\% PV\_CTR and +0.84\% UV\_CVR over the production baseline in online A/B tests.

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