Performance of similar MIP models

This is a table with the running times to solve a MIP model with 3000 binary variables. We stop as soon as the gap is 0.05%. The models only differ by a single parameter λ which changes the objective. This table shows how much difference in performance we can see just by going from λ=0.83 to λ=1. This is a good example where just changing one number in the same model can make an easy model very difficult to solve.

λ	obj	nodes	iterations	time
0%	19.15	29	477	5.12
4%	19.03	45	426	2.16
8%	18.91	64	513	3.63
13%	18.82	76	611	3.40
17%	18.76	18	657	2.78
21%	18.75	13	549	2.95
25%	18.76	17	763	4.74
29%	18.80	28	830	4.39
33%	18.92	21	704	4.46
38%	19.04	168	1298	6.70
42%	19.25	234	1234	7.12
46%	19.56	642	3686	17.69
50%	19.94	886	4601	16.12
54%	20.64	2292	21567	29.26
58%	21.42	1216	13781	16.64
63%	22.31	3233	34883	34.76
67%	23.47	1873	23906	29.30
71%	24.75	7286	54077	78.80
75%	26.31	5939	54634	82.18
79%	28.04	9486	85297	112.49
83%	29.78	7701	62220	97.78
88%	31.66	26102	202141	332.58
92%	33.65	83790	849745	987.04
96%	35.84	102327	911254	1377.03
100%	38.26	822222	6998739	11749.83