OpenAI's ChatGPT is a powerful language generation model, but it may not be the best fit for every use case. In this article, we'll look at some ChatGPT alternatives that can also be used for natural language processing tasks like text generation, language translation, and more.

1) GPT2: Developed by OpenAI, GPT-2 is a model similar to ChatGPT in that it can generate more coherent and realistic text. It does, however, necessitate more computational resources to run.

2) T5: Google's T5 model is yet another text generation model. It uses a transformer architecture, like GPT-2 and ChatGPT, but it is pre-trained on a variety of tasks, making it more versatile.

3) BERT: BERT is a Google transformer-based model designed specifically for natural language understanding tasks such as question answering and sentiment analysis. It is not designed specifically for text generation, but it can be fine-tuned for the task.

4) XLNet: XLNet is a new model that outperforms BERT and GPT-2 on a variety of natural language understanding tasks. It is trained using a novel permutation-based training method that allows it to better handle word dependencies in a sentence.

5) RoBERTa: RoBERTa is a recent model that improves on the pre-training approach used in BERT. Its architecture is similar to that of BERT, but it is pre-trained on a much larger dataset and fine-tuned on a wide range of tasks.

Finally, the best model for your specific use case will be determined by your project's specific requirements and constraints, such as computational resources and desired performance. It is also necessary to consider the model's cost, as some alternatives to ChatGPT are not open source and may require a paid licence.

Aside from the models mentioned above, there are several other ChatGPT alternatives that can be used for natural language processing tasks. Some examples are:

6) Megatron: Megatron is a large-scale transformer model developed by NVIDIA that can be fine-tuned for a variety of NLP tasks. It is well-suited for large-scale projects because it is optimised for GPU training and can handle large amounts of data.

7) CTRL (Conditional Transformer Language Model): CTRL is a conditional language model developed by Salesforce that can be fine-tuned for a variety of NLP tasks. It is intended to handle structured data like tables and can be used for tasks like text summarization and question answering.

8) Hugging Face's Funnel Transformer: Hugging Face's Funnel-Transformer is a lightweight version of transformer models such as GPT-2 and BERT that allows for faster training and inference. It is ideal for environments with limited resources, such as embedded systems and edge devices.

9) PEGASUS: PEGASUS is a Google-developed transformer-based model that can generate text in multiple languages. It has been pre-trained on a wide variety of text data, including books, articles, and websites, making it well-suited for cross-linguistic tasks.

10) T-NLG T-NLG is a transformer-based model developed by OpenAI that can generate text in multiple languages. It has been pre-trained on a wide variety of text data, including books, articles, and websites, making it well-suited for cross-linguistic tasks.

11) Transformer-XHT: Developed by Facebook, it is a transformer architecture extension that can generate text in multiple languages. It has been pre-trained on a wide variety of text data, including books, articles, and websites, making it well-suited for cross-linguistic tasks.

It is important to note that these models are used for more than just text generation; they are also used for sentiment analysis, question answering, and text summarization. These models may also differ in complexity and computational requirements, so it's critical to carefully weigh the trade-offs before deciding on a model for your specific use case.