January 14, 2024

How To Choose an OpenAI Alternative LLM in 2024?

Are you looking for alternatives to OpenAI’s GPT LLM and don’t know where to start? Our article details OpenAI alternatives companies can use to improve their daily operations.

Written by
Serhii Uspenskyi
CEO

Table of Contents

The competition among AI developers has never been higher. In 2022, the market for LLMs alone rose to $10.5 billion and is expected to grow to $40.8 billion by 2029. As was the case in the past, OpenAI has a chance to remain one of the frontrunners in this race, leading in investments and use cases with its advanced LLM products.

Companies found ways to integrate GPT to ease content creation, finding and hiring new employees, legal document analysis, onboarding, upskilling, and marketing efforts. While the LLM is extremely capable, there might be better choices for e-commerce, retail, and legal businesses.

Let’s not forget that LLMs are divided into open-source and closed-source. Previously, we discussed pros and cons of open source vs closed source LLMs. We investigated that OpenAI’s GPT is not the only option to use in your AI product as it has its pluses and minuses as any other LLM.

It’s one of the main reasons companies search for alternatives to Open AI in text-to-text LLMs. This guide dives deep into this issue, explains the features of alternative LLMs, and provides examples of free and paid GPT alternatives companies should explore before selecting the right combination.

Why Companies Look For Alternatives To Open AI?

There are many different reasons to use a specific LLM in a particular case: product architecture, quantity of users, data storage, existing software capabilities, and so on. Obviously OpenAI stands as the front-liner of LLM development and helps many businesses to build custom AI chatbots via using API and ChatGPT integration.

With the release of GPT-4o, hundreds of companies started to incorporate this LLM due to its impressive new features and functionality. However, we should not focus only on one point.

Each business has reasons why the GPT lineup of large language models might not be optimal for their organization. The most common of them include:

  1. Implementation costs. Smaller and medium-sized organizations don’t have the funds to enhance their products with this LLM, especially regarding training and computational costs. This is without paying the $20/month fee on ChatGPT Plus subscription.
  2. High computational resources. GPT products require a lot of processing power to work. This urges companies to find solutions that require fewer resources and perform required tasks. These are found in many OpenAI alternatives free of charge and with an open source.
  3. Limited capabilities. To optimize daily operations, companies combine several LLMs for maximum effect. They use one component to forecast profits and another to summarize documents or make content.
  4. Regulatory compliance. An organization may need an LLM that works following the guidelines on using and storing information. Alternatives can better align with these requirements and help organizations avoid legal problems.
  5. Security issues. Since OpenAI’s LLM gathers fast amounts of company data, organizations may be reluctant to share this information. As such, they require models with additional control over training data and other security measures.

Must-Have OpenAI Alternative Features

Many models work the same way as the GPT lineup. Companies should look for particular features when researching them before deciding on specific use cases and requirements for certain organizations.

  • Adaptability. Great examples of OpenAI product alternatives handle specific tasks through fine-tuning. The LLM must adapt to the organization's terminology and content styles.
  • Cost of use. When searching for GPT alternatives, carefully study the licensing terms and prices. Some LLMs are free for all purposes, while others require usage-based payments or subscriptions.
  • Learning through feedback. Advanced large language models adjust their responses by getting user feedback. These corrections improve the accuracy of LLMs over time.
  • Multi-turn conversations. Any alternative to OpenAI LLM must have this feature to make interactions more dynamic. Like GPT, it must talk to users like a real-life professional.
  • Privacy. Large language models work with tons of personal information. As such, they must have excellent security features to keep data safe and out of reach of hackers.
  • Retention. An LLM should be able to follow the context of conversations without losing their thread. The ability to handle multiple users and understand what they need are signs of an excellent replacement for GPT.
  • Scalability. Good large language models that take on OpenAI’s solution evolve with the organization while offering consistent performance.
  • Support. Lastly, the LLM makers should offer assistance if anything goes wrong with the product. Check for a support system and a community of users. This way, it will be possible to address issues on time.

Have a question?

Best OpenAI Alternatives: Free And Paid

  1. Gemini (Google)

Gemini LLM is an advanced large language model designed to facilitate a wide range of natural language processing tasks. Developed to understand and generate human-like text, Gemini LLM leverages cutting-edge neural network architectures and extensive training datasets to achieve high levels of accuracy and fluency.

Its capabilities include text completion, summarization, translation, and conversational AI, making it a versatile tool for applications in customer service, content creation, and research.One of the standout features of Gemini LLM is its ability to continually improve through feedback and real-world data integration. This adaptive learning process ensures that the model remains up-to-date with the latest language trends and domain-specific knowledge. Additionally, Gemini LLM emphasizes ethical AI practices, incorporating safeguards to minimize biases and ensure responsible usage. This makes it a reliable choice for businesses and developers looking to implement sophisticated AI-driven solutions while maintaining a commitment to ethical standards.

  1. Claude 2 (Anthropic)

Released in the summer of 2023, the next version of Antropic’s LLM offers several improvements over its predecessor. Claude 2 gives more accurate and extended responses and supports API integration.

Businesses add it to existing pieces of software to provide helpful, harmless, and precise answers to customers and generate high-quality content.In addition to writing engaging marketing content and blog posts, the tool eases the documentation process. Companies use Claude 2 to write letters and memos, summarize files, compare them, and forecast industry trends. Anthropic offers the power of Claude 2 for $11.02 per 1 million tokens, making it an excellent choice for businesses with high AI demands.

  1. Cohere (Cohere)

Developed by former Google employees, the Cohere LLM is one of the best alternatives to Open AI’s model. Its creators made a tool specifically for generative AI use for corporations and enterprises. This model comes in different sizes, with the smallest working with 6 billion parameters and the largest having 52 billion.Cohere has some of the highest scores among the models in terms of accuracy. Companies like Jasper, HyperWrite, and Spotify use this LLM to enhance users' experience. While Cohere shows excellent results, it's a pricy model, charging $15 per 1 million tokens.

  1. Falcon (Technology Innovation Institute)

Experts from Dubai’s Technology Innovation Institute develop Open AI alternatives that are free and open-sourced. Falcon is an autoregressive model trained on vast volumes of code and text from different languages and dialects. Thanks to its advanced architecture, the model handles data processing and prediction more effectively.Using Falcon in business opens new automation opportunities. This LLM helps them enter new markets and successfully communicate with international clients thanks to multilingual support. TII’s model can also create a unique marketing experience for customers and potential partners.

  1. Guanaco (Guanaco)

This open-source LLM is based on Meta’s LLaMA solution. The model performs well and is more efficient than decoder-only alternatives like GPT-3 and GPT-4. It requires fewer resources for output generation and produces more accurate results. Guanaco has four versions: 7 billion, 13 billion, 33 billion, and 65 billion parameters. The large language model supports several languages, including Japanese, German, German, and Chinese. Its latest version holds extensive conversations with users, avoids erroneous responses, or rejects answering questions it doesn’t have answers to. Guanaco also analyzes uploaded images and provides textual information about their content.

  1. LLaMA 2 (Meta)

Mark Zuckerberg’s Meta made the new version of its LLM available last year for free research and commercial use. LLaMA is under development but already demonstrates good results with reading, natural language comprehension, and performing various tasks.

While Meta developed the LLM to enhance educational applications, it has many uses in the business world.Companies add LLaMA 2 to improve user experience and product sales. The large language model helps better analyze the market, improve customer service, and generate engaging content. Additionally, it's possible to use Meta’s LLM to train employees and improve their skills.

  1. Mistral (Mistral AI)

The French company Mistral AI developed this alternative to OpenAI’s GPT. The model is smaller than other alternatives to OpenAI’s GPT (7 billion parameters) but has the same computational capabilities. They allow Mistral to produce text output faster and use fewer resources.The model offers the same perks as Meta’s LLaMA regarding business benefits. It handles various language-related tasks, streamlining text summarization, generation, analysis, and classification. These features make it indispensable for content, customer support, and analytical tasks.

  1. MPT-30B (Mosaic LM)

This player in the LLM market combines the capabilities of Camel-AI, GPTeacher, ShareGPT, and Baize to outperform many of its rivals. MPT-30B allows programmers to fine-tune the LLM for specific needs or train it from scratch. The computational capabilities of MPT-30B rival those of GPT-3, LLaMa, and Falcon.The LLM product at Mosaic LM shows better reasoning, reading capabilities, and language comprehension. It has a large context window and produces up to 7000 words of output per request. MPT-30B uses various sources, such as Wikipedia and Semantic Scholar, as its sources of information.

  1. Orca (Microsoft)

This LLM developed by Microsoft’s researchers uses progressive learning to improve itself using information from other LLMs. In this process, larger models serve as teachers, and Orca is the student that mimics their behavior. The alternative to OpenAI’s family shows how open-sourced models compete successfully with their closed counterparts.

  1. PaLM 2 (Google)

This Google-developed model fuels the corporation’s Bard AI conversational tool. It’s considered one of the top OpenAI rivals in 2024. The LLM boasts excellent results in logic, reasoning, knowledge of riddles, idioms, and nuanced texts in various languages.These qualities let PaLM 2 successfully compete with GPT’s latest version. The LLM has instant responses and offers three answers to each question. Google’s LLM also solves mathematical problems and is versatile in over 20 programming languages, making PaLM 2 an excellent developer helper.

  1. StableLM (Stability AI)

Stability AI’s LLM comes in different sizes (from 7 to 175 billion parameters). While the company focuses on image generation with Stable Diffusion, its text model shows great promise. Its StableLM product works with various text sources and demonstrates deep language comprehension and interpretation capabilities.Stable AI performs various language-related tasks, including sentiment analysis and text summarization. The large language model is equally scalable, allowing it to adapt to increasing workloads and a growing user bases.

Springs Expert Opinion

Springs has already implemented many different LLMs, including GPT-3.5, GPT-4, and GPT-4 alternatives. Our clients often ask us for advice: whether to choose OpenAI ChatGPT Integration or Open-source LLMs, like LLaMa 3. What we say is that every option has its advantages and disadvantages. 

For instance, the pricing for GPT4o is 5,00 USD / 1M tokens (input) and 15,00 USD / 1M tokens (output), which seems to be a lot if you have a large business with thousands of inquiries daily. So, many business owners prefer choosing open-source LLMs that they think will be free of charge and they will save money.

However, they forget about a really important point - hosting open-source LLM on the server and processing it on the server. According to our previous calculations, hosting the Hugging Face Llama 3-8B model on Google Cloud Platform's Compute Engine will cost you around 20 USD/hr which is around 3200$/monthly if you use it for 20 business days only.

In other words, every business case is unique and it is worth spending some time on a technical consultation to get a better idea of what kind of LLM is better to use in your particular situation. Springs experts are happy to help you with it. Feel free to contact us and we will guide you on this topic.

Final Thoughts

OpenAI’s GPT family is one of a kind due to the sheer volume of information used in training its large language models. Despite this, LLMs in our guide present a viable alternative to its capabilities and even surpass them in some areas. It all comes down to what your company needs an LLM for and whether it can afford to pay for its use.

Given the considerations above, we advise businesses to seek technical consultations to determine the most suitable LLM for their specific needs. They highlight that every business case is unique, and an informed decision requires understanding both the financial implications and the technical requirements of different LLMs. Springs' experts are available to provide guidance on this matter, helping companies navigate the complexities of choosing between commercial and open-source LLM options. Ultimately, the choice depends on the company’s needs and budget for leveraging LLM technology.

Customer retention is the key

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What are the most relevant factors to consider?

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Don’t overspend on growth marketing without good retention rates

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What’s the ideal customer retention rate?

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Next steps to increase your customer retention

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