Haplotypes are the units of inheritance in an organism, and many genetic analyses depend on their precise determination. Methods for haplotyping single individuals use the phasing information available in Next Generation Sequencing reads, by matching overlapping SNPs while penalizing post hoc nucleotide corrections made. Haplotyping diploids is relatively easy, but the complexity of the problem increases drastically for polyploid genomes, which are found in both model organisms and in economically relevant plant and animal species. While a number of tools are available for haplotyping polyploids, the effects of the genomic makeup and the sequencing strategy followed on the accuracy of these methods have hitherto not been thoroughly evaluated. We developed the simulation pipeline haplosim to evaluate the performance of haplotype estimation algorithms for polyploids: HapCompass, HapTree and SDhaP, in settings varying in sequencing approach, ploidy levels and genomic diversity, using tetraploid potato as the model. Our results show that sequencing depth is the major determinant of haplotype estimation quality, that 1kb PacBio CCS reads and Illumina reads with large insert-sizes are competitive, and that all methods fail to produce good haplotypes when ploidy levels increase. Comparing the three methods, HapTree produces the most accurate estimates, but also consumes the most resources. There is clearly room for improvement in polyploid haplotyping algorithms.