We propose a combination of character-based and word-based language models in an end-to-end automatic speech recognition (ASR) architecture. In our prior work, we combined a character-based LSTM RNN-LM with a hybrid attention/connectionist temporal classification (CTC) architecture. The character LMs improved recognition accuracy to rival state-of-the-art DNN/HMM systems in Japanese and Mandarin Chinese tasks. Although a character-based architecture can provide for open vocabulary recognition, the character-based LMs generally under-perform relative to word LMs for languages such as English with a small alphabet, because of the difficulty of modeling Linguistic constraints across long sequences of characters. This paper presents a novel method for end-to-end ASR decoding with LMs at both the character and word level. Hypotheses are first scored with the character-based LM until a word boundary is encountered. Known words are then re-scored using the word-based LM, while the character-based LM provides for out-of-vocabulary scores. In a standard Wall Street Journal (WSJ) task, we achieved 5.6 % WER for the Eval'92 test set using only the SI284 training set and WSJ text data, which is the best score reported for end-to-end ASR systems on this benchmark.