diff --git a/README.md b/README.md
index 66a82471dccad9805df09407c2a351a63654a0e6..b80b1629d49040dede35a05fb8d2a6689918d7ac 100644
--- a/README.md
+++ b/README.md
@@ -62,17 +62,21 @@ You may also needs to `pip install theano`.
 ```shell
 $ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides SIINFEKL SIINFEKD SIINFEKQ
 allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high
-HLA-A0201,SIINFEKL,6029.079749556217,4474.10333152741,7771.2922076773575
-HLA-A0201,SIINFEKD,18950.310303704624,15317.127851792027,22490.05728778504
-HLA-A0201,SIINFEKQ,18776.978315260818,14899.359763218705,22314.737180384865
-HLA-A0301,SIINFEKL,25589.66470369661,22962.4956808368,29395.86949262485
-HLA-A0301,SIINFEKD,25753.619337400796,22851.89399578629,29347.659901990868
-HLA-A0301,SIINFEKQ,26870.51318688641,24198.39885651102,30364.15208364084
+HLA-A0201,SIINFEKL,5326.541919062165,3757.86675352994,7461.37693353508
+HLA-A0201,SIINFEKD,18763.70298522213,13140.82000240037,23269.82139560844
+HLA-A0201,SIINFEKQ,18620.10057358322,13096.425874678192,23223.148184869413
+HLA-A0301,SIINFEKL,24481.726678691946,21035.52779725433,27245.371837497867
+HLA-A0301,SIINFEKD,24687.529360239587,21582.590014592537,27749.39869616437
+HLA-A0301,SIINFEKQ,25923.062203902562,23522.5793450799,28079.456657427705
 ```
 
 The predictions returned are affinities (KD) in nM. The `prediction_low` and
-`prediction_high` fields give the 5-95 percentile predictions across the models 
-in the ensemble.
+`prediction_high` fields give the 5-95 percentile predictions across the models
+in the ensemble. The predictions above were generated with MHCflurry 0.9.2.
+Your exact predictions may vary slightly from these (up to about 1 nM)
+depending on the Keras backend in use and other numerical details even if you
+match the MHCflurry version, whereas different versions of MHCflurry give
+results that are considerably different from these.
 
 You can also specify the input and output as CSV files.
 Run `mhcflurry-predict -h` for details.