Malware infects thousands of systems globally each day causing millions of dollars in damages. Which disassembler should a malware analyst choose in order to get the most accurate disassembly and be able to detect, analyze and defuse malware quickly? There is no clear answer to this question: (a) the performance of disassemblers varies across configurations, and (b) most prior work on disassemblers focuses on benign software and the x86 CPU architecture. In this work, we take a different approach and ask: why not use all the disassemblers instead of picking one? We present DisCo, a novel and effective approach to harness the collective capability of a group of disassemblers combining their output into an ensemble consensus. We develop and evaluate our approach using 1760 IoT malware binaries compiled with different compilers and compiler options for the ARM and MIPS architectures. First, we show that DisCo can combine the collective wisdom of disassemblers effectively. For example, our approach outperforms the best contributing disassembler by as much as 17.8% in the F1 score for function start identification for MIPS binaries compiled using GCC with O3 option. Second, the collective wisdom of the disassemblers can be brought back to improve each disassembler. As a proof of concept, we show that byte-level signatures identified by DisCo can improve the performance of Ghidra by as much as 13.6% in terms of the F1 score. Third, we quantify the effect of the architecture, the compiler, and the compiler options on the performance of disassemblers. Finally, the systematic evaluation within our approach led to a bug discovery in Ghidra v9.1, which was acknowledged by the Ghidra team.