ECM is a probabilistic algorithm. Its probability of success depends on the size of the (usually unknown) factor p to be found, on the step 1 and 2 bounds B1 and B2, and possibly on some other implementation-dependent parameters.

The table below indicates for each factor size what is the optimal step 1 limit B1 to use (first argument of the GMP-ECM program). The column "expected curves" corresponds to the default parameters of GMP-ECM. Those figures can be reproduced using thedigits | optimal B1 | expected curves (default parameters for GMP-ECM 6) |
expected curves (default parameters for GMP-ECM 7) |

20 | 11,000 | 86 | 107 |

25 | 50,000 | 214 | 261 |

30 | 250,000 | 430 | 513 |

35 | 1,000,000 | 910 | 1,071 |

40 | 3,000,000 | 2,351 | 2,753 |

45 | 11,000,000 | 4,482 | 5,208 |

50 | 43,000,000 | 7,557 | 8,704 |

55 | 110,000,000 | 17,884 | 20,479 |

60 | 260,000,000 | 42,057 | 47,888 |

65 | 850,000,000 | 69,471 | 78,923 |

70 | 2,900,000,000 | 102,212 | 115,153 |

75 | 7,600,000,000 | 188,056 | 211,681 |

80 | 25,000,000,000 | 265,557 | 296,479 |