Abstract Single‐cell spatial transcriptomics enables comprehensive gene expression profiling with precise cellular localization within tissue architecture. To systematically evaluate the compatibility and performance of alternative sequencing platforms for this application, we directly compared Illumina NovaSeq 6000 and GeneMind SURFSeq 5000 using SeekSpace single‐cell spatial transcriptomics on mouse brain and lung tissues. Identical cDNA libraries were sequenced on both platforms and processed with a unified bioinformatics pipeline to ensure direct comparability. Across all key sequencing quality metrics—including unique molecular identifier and spatial barcode detection, gene identification, and mapping rates—SURFSeq 5000 demonstrated performance highly similar to NovaSeq 6000, with nearly equivalent quality control metrics and data yields. Integrated downstream analyses—including dimensionality reduction, cell type annotation, spatial mapping, differential gene expression, cell–cell interaction, and spatial hotspot module detection—revealed highly concordant spatial patterns and cellular compositions across both brain and lung tissues. The overlap of differentially expressed genes between platforms reached approximately 65%, and cross‐platform cell type assignments showed high reproducibility (Area Under the Receiver Operation Characteristic curve > 0.92). No significant batch effects were observed. These results demonstrate that GeneMind SURFSeq 5000 is a reliable and cost‐effective alternative to Illumina NovaSeq 6000 for single‐cell spatial transcriptomics, providing comparable data quality and analytical robustness in murine tissue studies. The robust performance of SURFSeq 5000 supports the broader adoption of alternative and affordable sequencing technologies in spatial omics research.