This paper investigates the impact of different types and size of training corpora on language models. By asking the fundamental question of quality versus quantity, we compare four French corpora by pre-training four different ELMos and evaluating them on dependency parsing, POS-tagging and Named Entities Recognition downstream tasks. We present and asses the relevance of a new balanced French corpus, CaBeRnet, that features a representative range of language usage, including a balanced variety of genres (oral transcriptions, newspapers, popular magazines, technical reports, fiction, academic texts), in oral and written styles. We hypothesize that a linguistically representative corpus will allow the language models to be more efficient, and therefore yield better evaluation scores on different evaluation sets and tasks. This paper offers three main contributions: (1) two newly built corpora: (a) CaBeRnet, a French Balanced Reference Corpus and (b) CBT-fr a domain-specific corpus having both oral and written style in youth literature, (2) five versions of ELMo pre-trained on differently built corpora, and (3) a whole array of computational results on downstream tasks that deepen our understanding of the effects of corpus balance and register in NLP evaluation.