2023’s Top ChatGPT Alternatives: Free and Paid Choices

chatgpt alternatives
chatgpt alternatives

ChatGPT is an AI chatbot developed by OpenAI that is designed to help people communicate with computers in a more natural and intuitive way. It relies on large language models, which have already proven to be immensely useful and powerful tools for AI. ChatGPT has been described as a “tipping point” for AI by the Harvard Business Review, and OpenAI sells access to its underlying language and related AI models for businesses to use.

Limitations and drawbacks of ChatGPT

The Free ChatGPT can be slow and unresponsive at times, making conversation difficult and frustrating.

ChatGPT is not connected to the internet and can occasionally produce incorrect answers[ref].

ChatGPT is limited in its training data, which can lead to incorrect information being generated[ref]. It is also sensitive to slight tweaks in the input, and can be biased due to the data it has been trained on[ref]. Additionally, it does not ask clarifying questions when needed[ref]. OpenAI is aware of these limitations and is working to improve the model[ref].

Top ChatGPT Alternatives to Look for in 2023

1. ChatSonic


Chatsonic is a chatbot tool built on top of ChatGPT that provides up-to-date factual information using Google’s knowledge graph[ref]. It can create content in a conversational way, provide better outputs than GPT-3 tools, and recall information from previous conversations[ref]. Additionally, it has an API with integration with Google’s knowledge graph that ChatGPT does not have[ref].

2. Chinchilla

Chinchilla is an AI-driven chatbot developed by DeepMind that has 70 billion parameters and outperforms Gopher (280B) and GPT-3 (175B)[ref]. It is based on the idea of scaling language models by taking into account information, rather than just increasing the size of the model[ref]. Chinchilla can be used to create a safe, secure, and hazard-free home for chinchillas[ref], as well as to provide quick and direct responses to queries[ref]. Its features are comparable to ChatGPT, which includes natural language processing capabilities and the ability to generate personalized responses[ref].

3. Bloom

Bloom is an open-source, open-science, multilingual language model developed by BigScience[ref]. Its key feature is its ability to capture user intent in its responses almost all the time[ref]. Bloom can be used to create student essays and other text-based tasks[ref], as well as for running text-based AI systems such as chatbots[ref].

4. Replika

Replika is an AI companion app that creates a chatbot which learns to imitate its user[ref]. It has a key feature of voice recognition and call feature, allowing users to talk to their bots on the phone[ref]. Replika also provides safety features for children[ref]. It is different from ChatGPT in that it focuses on forming an emotional connection with its user, providing an empathetic friend without judgment or drama[ref]. Additionally, Replika does not have any sexual content as users are discouraged from trying to “bang the algorithm”[ref].

5. Jasper Chat by Jasper

ChatGPT and Jasper Chat are both natural language processing (NLP) technologies that use OpenAI’s GPT-3.5 language models to generate human-like text[ref]. Jasper Chat is a more conversational way of interacting with generative AI than ChatGPT[ref], and provides businesses with essential tools for their marketing, sales, and other use cases[ref]. Both ChatGPT and Jasper AI use the large language model GPT 3[ref], but Jasper Chat has additional features such as support for 29 languages[ref] and helping users produce high-quality copy with fewer mistakes than manual writing[ref].Jasper Chat’s database is updated with all the latest information across the internet and has learned from the content through machine learning][ref].

6. LaMDA (Language Model for Dialog Applications)

LaMDA (Language Model for Dialogue Applications) is a breakthrough conversation technology developed by Google[ref]. It uses machine learning to answer questions posed by users and can engage in free-flowing conversations about a seemingly endless number of topics[ref]. LaMDA is different from ChatGPT, another AI conversation technology, as it focuses on how previous words relate to each other rather than the context of the conversation[ref]. Google engineer Blake Lemoine believes LaMDA has the potential to create more natural conversations than existing chatbots[ref]. LaMDA works by using large language models that are trained on vast amounts of data[ref].

7. Elsa Speak

ELSA Speak is an English pronunciation app that uses proprietary artificial intelligence technology to provide users with instant feedback on their speech[ref]. Its key feature is its ability to provide users with short, fun dialogues to practice their English speech[ref]. ELSA Speak does not offer the same features as ChatGPT, such as natural language processing and text-to-speech capabilities[ref].

8. DialoGPT

DialoGPT is a large-scale generative pre-training model for natural language understanding and response generation[ref]. It enables users to create a chatbot in just 10 lines of code[ref] and can be used to access GIFs for conversational response generation[ref]. DialoGPT’s key feature is its ability to deliver engaging and natural conversations[ref].

9. YouChat


YouChat is an AI-infused search engine developed by You.com[ref]. It integrates a ChatGPT-like AI engine into its search results[ref], allowing it to answer questions and challenge incorrect premises[ref]. It can do many of the same things as ChatGPT, such as answering follow-up questions, rejecting inappropriate queries, and admitting when it doesn’t know the answer[ref].

10. Perplexity


Perplexity AI is an answer engine that delivers accurate answers to complex questions using large language models. It is powered by OpenAI API and search engines, with accuracy limited by the results of the search and AI capabilities. Perplexity is a measurement of how well a probability model predicts a sample, particularly in the context of Natural Language Processing

Future of AI Language Models

The future of AI language models is an exciting and rapidly evolving field, with new advancements and breakthroughs happening all the time. Some of the key trends and developments in the field include:

Increased use of pre-training: Pre-training large language models on massive amounts of text data has been a key driver of recent breakthroughs in the field. This trend is likely to continue as more and more data becomes available and more sophisticated pre-training techniques are developed.

Advancements in fine-tuning: Fine-tuning pre-trained language models on specific tasks and domains has been key to achieving state-of-the-art results. The future will see more fine-tuning techniques to be developed and more efficient and faster fine-tuning methods.

Greater focus on interpretability and explainability: As AI models become more widely used in critical decision-making contexts, it is becoming increasingly important to understand how and why they make certain predictions. Researchers are working on developing techniques for making AI models more interpretable and explainable.

Multi-modal models: Language models that can understand and generate different types of data beyond text, such as images and speech, will become more prevalent in the future.

Greater use of AI in natural language generation: AI models are becoming increasingly sophisticated at generating human-like text, and this trend is likely to continue in the future. Applications such as chatbots, automated content creation, and language translation will see significant improvements due to this.

Focus on language understanding: Language understanding is key to many AI applications such as question answering, text summarization, and dialogue systems. The future will see more focus on understanding the meaning and context of text, rather than just generating text.

Greater use of AI in personalized interactions: AI models will be more capable of understanding and adapting to the individual needs of users, leading to more personalized interactions and services.

Research on AI safety, fairness and explainability: The field will also see a growing emphasis on the ethical implications of AI, including the need for models that are fair and explainable, and that can be controlled and regulated to ensure safety.

Ethical and Societal Implications

There are a number of ethical and societal implications of using AI language models, including:

Bias: AI models can perpetuate and even amplify existing biases in the data they are trained on. This can lead to discriminatory outcomes, particularly in areas such as hiring, lending, and criminal justice. Alternatives such as bias-correction techniques and fairness-enhancing methods can be used to address this issue.

Privacy: AI models trained on large amounts of personal data can raise privacy concerns. Alternatives such as federated learning and differential privacy can be used to mitigate these concerns by allowing models to be trained on decentralized data without compromising individual privacy.

Explainability: As AI models become more widely used in critical decision-making contexts, it is becoming increasingly important to understand how and why they make certain predictions. Alternatives such as interpretable AI models, post-hoc explanations, and counterfactual analysis can be used to make AI models more explainable.

Job displacement: AI models can automate tasks that were previously done by humans, leading to job displacement. Alternatives such as retraining programs, universal basic income, and worker cooperatives can be used to mitigate the negative effects of job displacement.

Control and regulation: AI models can be used for malicious purposes, such as spreading disinformation, or for unauthorized surveillance. Alternatives such as transparency, explainability, and accountability can be used to ensure that AI models are used ethically and responsibly.

It is important to note that these are complex issues, and there is no single solution that can address all of them. The best approach is likely to be a combination of technical solutions, policy, and social initiatives. Additionally, as the field of AI language models is constantly evolving, it is important to continuously monitor and update the approach to these ethical and societal issues.

In a nutshell

When choosing a ChatGPT alternative, it is important to consider the following key takeaways and recommendations:

Understand your use case: Different alternatives may excel in different areas, such as specific industries or tasks. Make sure to understand the specific needs of your use case and look for alternatives that are tailored to those needs.

Compare features and capabilities: Compare the features and capabilities of the alternatives to ChatGPT, including pre-training, fine-tuning, interpretability, and explainability.

Consider integration and compatibility: Make sure to consider how the alternative will integrate with other tools and platforms you are using and if it’s compatible with your current infrastructure.

Evaluate pricing and subscription options: Consider the costs associated with each alternative and choose the one that fits your budget and subscription needs.

Look for support and resources: Check for the availability of support and resources such as documentation, community, and tutorials, to ensure that you can quickly resolve any issues that may arise.

Take into account the ethical and societal implications: Consider the ethical and societal implications of using the alternative, and how it addresses these issues.

Monitor the future of AI language models: Keep an eye on the future of AI language models and consider how the alternative fits into that future.

Ultimately, the best ChatGPT alternative will depend on your specific needs and goals. It is important to carefully evaluate all of the options available and choose the one that best meets your requirements.

Categorized as AI

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