diff --git a/src/basicfuns.jl b/src/basicfuns.jl
index 5c39104..0769860 100644
--- a/src/basicfuns.jl
+++ b/src/basicfuns.jl
@@ -63,8 +63,8 @@ Return `log(1+exp(x))` evaluated carefully for largish `x`.
 This is also called the ["softplus"](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))
 transformation, being a smooth approximation to `max(0,x)`. Its inverse is [`logexpm1`](@ref). 
 """
-log1pexp(x::Real) = x < 18.0 ? log1p(exp(x)) : x < 33.3 ? x + exp(-x) : oftype(exp(-x), x)
-log1pexp(x::Float32) = x < 9.0f0 ? log1p(exp(x)) : x < 16.0f0 ? x + exp(-x) : oftype(exp(-x), x)
+log1pexp(x::Real) = x ≤ -37. ? exp(x) : x ≤ 18. ? log1p(exp(x)) : x ≤ 33.3 ? x + exp(-x) : float(x)
+log1pexp(x::Float32) = x < 9.0f0 ? log1p(exp(x)) : x < 16.0f0 ? x + exp(-x) : x
 
 """
     log1mexp(x::Real)
diff --git a/test/basicfuns.jl b/test/basicfuns.jl
index 02e00f4..03d7984 100644
--- a/test/basicfuns.jl
+++ b/test/basicfuns.jl
@@ -27,11 +27,13 @@ end
     @test log1pexp(-2.0)   ≈ log(1.0 + exp(-2.0))
     @test log1pexp(10000)  ≈ 10000.0
     @test log1pexp(-10000) ≈ 0.0
-
     @test log1pexp(2f0)      ≈ log(1f0 + exp(2f0))
     @test log1pexp(-2f0)     ≈ log(1f0 + exp(-2f0))
     @test log1pexp(10000f0)  ≈ 10000f0
     @test log1pexp(-10000f0) ≈ 0f0
+    @inferred log1pexp(0)
+    @inferred log1pexp(0.)
+    @inferred log1pexp(0.0f0)
 end
 
 @testset "log1mexp" begin