The divergence can actually come from a whole range of differences between the different forecasters.
The biggest one is probably in the resolution of the model (if you imagine that an area, say the UK, is divided up into little squares, the computer model solves a whole lot of equations for each square).
If the square is 5km by 5km in size then some processes, and a lot of the topography will be smoothed out, but if the square is 500m by 500m then a lot more will be captured. Imagine a hill of 1000m rising out of a flat plain at 0m elevation is in a particular location. The elevation of the square is the average of the whole elevation. In the 5km by 5km model, the entire hill and an equal area of plain is captured so in the square the average elevation may be 500m, but in the 500m by 500m model it may need 4 squares to accurately cover the hill alone and each square will have a different elevation.
Temperatures typically go down as you go higher, and rain will fall as the air cools, so if the hill isn't "resolved" in the model the prediction may be unrealistically warm and dry. This is why forecasters like high resolution models, but that resolution comes at a high cost, because you need to increase the vertical resolution (the number of squares in the atmosphere) and reduce the length of time between each calculation as the horizontal resolution increases.
Another source of difference are the actual equations solved in the model. These can be formulated in different ways and with different approximations and are often "tuned" so a model that works really well in Scotland is unlikely to be so successful in the Sahara (where I imagine forecasting is actually pretty easy - it'll be hot and sunny ). That's not to say the models are "wrong" just that they perform better in some circumstances than others and are designed for different purposes often.
Finally, a further important difference is the updating of the model with real time observations and at the boundaries. Most models are not run for the whole wold, but only a small portion (the whole world can be run at a resolution of about 20km nowadays, but it takes a lot of very expensive computer power). More effective is running a small section (say the UK), and telling the computer what is happening at the edges of the box based on satellite and ground observations. These can also be fed in to the area within the model to nudge it in the right direction. In practice, in Europe most national agencies get this information from a single source, the ECMWF, although I'm not so sure commercial forecasters have access to it. Also, the models tend to be "better" than the observations (as measurements can go wrong, be wrongly calibrated etc etc), so at any given moment a weighting of about 40% is applied to observations rather than 60% for the model, depending on what in particular you're looking at.
So the answer to your question is probably, it depends, on where you live and also how far in the future you need your weather to be reliable. Most forecasts aren't bad up to 3 days in advance in general terms but the specifics can change quickly. Beyond that to 5 days is more tricky and after that we're really just guessing (at least in a maritime climate like the UK).
I agree with OYBBK though, the met office is probably the best place to start, though for hill wlaking forecasts try the mountain weather service, they have a model specifically developed for tricky hillly terrain...
Here endeth the lesson (and you get extra credits for reading to the end ).