I would try a different approach than clustering.
For now, I tried L1 distance, cosine similarity, Euclidean distance, Mahalanobis Distance
First, you could have a look at approximate string matching measures. These are likely to give you much better similarity results on a pair of movie titles. It's usually a good idea to use not only word-based measures but also character-based or char n-grams based measures.
how can I compare them to see which method perform best?
A proper evaluation framework would require annotating manually a large amount of pairs of titles as similar/not similar (or even a degree of similarity). Unless you have a lot of time, this is completely impractical because there is certainly a massive imbalance between positive and negative pairs. So instead you could use bootstrapping, which means running a few similarity measures on your data, extract the top N pairs for each measure, then manually annotate only these. It's likely that this would give you a high amount of (rare) positive cases, and you can build a labeled dataset by assuming that other instances are negative. It's obviously a simplification, otherwise you can take the time to annotate a lot of negative cases as well (it's still much faster than without bootstrapping, since you already have your positive cases).
my purpose is to find the most similar movie titles, I want to use different distance/similarity measurements and compare them, what is the best method to use?
Based on the dataset you have built, you can now train a supervised model, with a pair of titles as instance. You can use various similarity measures as features, and you should vary the type of similarity (char-based, ngram-based, word-based) across these features in order to provide the model with a diversity of characteristics.
Then you can predict the similarity between any two pairs. This gives you a graph of similarity relations between all the movies, from which you can extract groups which are similar together.
Note that this is just a general strategy, many parts of it can be refined/adapted to your data and of course it depends how much time you want to spend on this problem.