Abstract Background Internal tandem duplications in the FLT3 gene, termed FLT3 -ITDs, are useful molecular markers in acute myeloid leukemia (AML) for patient risk stratification and follow-up. FLT3 -ITDs are increasingly screened through high-throughput sequencing (HTS) raising the need for robust and efficient algorithms. We developed a new algorithm, which performs no alignment and uses little resources, to identify and quantify FLT3 -ITDs in HTS data. Results Our algorithm (FiLT3r) focuses on the k -mers from reads covering FLT3 exons 14 and 15. We show that those k -mers bring enough information to accurately detect, determine the length and quantify FLT3 -ITD duplications. We compare the performances of FiLT3r to state-of-the-art alternatives and to fragment analysis, the gold standard method, on a cohort of 185 AML patients sequenced with capture-based HTS. On this dataset FiLT3r is more precise (no false positive nor false negative) than the other software evaluated. We also assess the software on public RNA-Seq data, which confirms the previous results and shows that FiLT3r requires little resources compared to other software. Conclusion FiLT3r is a free software available at https://gitlab.univ-lille.fr/filt3r/filt3r . The repository also contains a Snakefile to reproduce our experiments. We show that FiLT3r detects FLT3-ITDs better than other software while using less memory and time.