LLM сomparison: How to choose the right model for your business

Data privacy and security

When you run models like Llama or Mistral locally, all prompts, context, and content are processed entirely within your environment. You have full control over system security, including encryption, logging, and access policies. This is crucial when working with sensitive data and meeting standards like HIPAA, GDPR, and FINRA.

This does not mean cloud LLMs lack enterprise-grade security—leading providers adhere to major regulations. For example, OpenAI complies with GDPR, CCPA, AICPA, and ISO 27001; Anthropic supports SOC 2, HIPAA, and GDPR; and Google’s Gemini aligns with SOC 1/2/3, GDPR, ISO 27001, ISO 27017, ISO 27018, and ISO 27701.

Still, some models raise concerns about data protection. For example, DeepSeek was at the center of a major data security scandal in late 2024 after a large-scale malicious attack. In addition, some countries are wary of DeepSeek because it is trained with a Chinese worldview. This has led to DeepSeek being banned in several countries, including Australia, India, South Korea, Italy, and the US.

Cost-efficiency

When choosing an LLM, pricing is usually a key factor. While businesses typically seek cost-effective solutions, the most cost-effective isn’t always the cheapest. The goal should be to select a model that delivers tangible results over time. Otherwise, an apparently successful purchase may turn out to have hidden costs or performance issues.

For example, free models like Mistral Saba or Pixtral Small offer basic functionalities for small tasks but typically deliver lower performance, limited capabilities, and reduced security compared to premium options. It’s wiser to invest in a model that balances performance and protection to avoid future penalties, especially in highly regulated industries.

Another misconception is that bigger is better. A comprehensive LLM size comparison shows that smaller models can achieve impressive performance at a lower cost.

This table shows how dramatically pricing can vary, even between models from the same provider. Some models, like OpenAI’s o1 and Anthropic’s Claude Opus, are expensive but excel at advanced reasoning, deep contextual understanding, and accurate outputs. Their high cost can actually translate into significant savings by replacing expensive manual work or reducing error rates.

Self-hosted, open-weight models offer a different kind of cost-efficiency by eliminating per-token charges once deployed. However, they will still incur costs for infrastructure and maintenance, including a skilled engineering team to manage the environment and monitor resource usage, GPU server maintenance, and GPU runtime costs. Self-hosted models often provide long-term cost advantages for organizations with in-house IT teams.

Language coverage

Marketers need AI that speaks their customers’ language, so decision-makers pay particular attention to what languages an LLM supports. Today, the LLM model list includes options that come with extensive out-of-the-box multilingual training, such as Google’s Gemini and OpenAI’s o1, o3-mini, and GPT series. Their broad language coverage—as well as their high level of fluency, accuracy, and cultural alignment—makes them ideal for global companies.

Some models are tailored specifically for one area. A perfect example is Mistral Saba, which is designed for Middle Eastern and South Asian regions.

Llama and Mistral have limited language coverage and usually require additional adaptation. However, this is where open-source models excel. Active communities surrounding these LLMs frequently release fine-tuned versions for specific languages and dialects. For businesses operating in areas with underrepresented or niche languages, this flexibility provides a major advantage.

Customization

Open-source models not only provide flexibility for multilingual customization but can also be adapted to industry terminology and security policies.

Imagine a biotech company that requires a model familiar with scientific language and acronyms. Even high-performing general-purpose models may fall short of their expectations without additional training. For example, Anthropic’s Claude is known for its security, accuracy, speed, and suitability for sensitive applications like health-related industries, where it complies with numerous standards, including HIPAA. However, its limited customization options made this model series unsuitable for the requirements of a biotech company.

Meanwhile, open-weight models like Llama, Mistral, and DeepSeek-R1 can be fine-tuned on proprietary datasets, enabling them to understand specialized terminology and think within a domain-specific framework. This makes them an ideal choice for companies that require more than generic reasoning.

Integration and support

When selecting an LLM, it is important to evaluate not only its capabilities but also how easily it can be integrated into an organization’s existing system.

Cloud-based LLMs are ready to be deployed through APIs and require less time. Leading LLM providers like OpenAI and Anthropic offer not only fast setup but also clear documentation, software development kits (SDKs), and technical support, which makes them especially convenient for companies that want to adopt an LLM quickly.

Furthermore, cloud LLMs boast growing third-party ecosystems. OpenAI’s models are supported by a wide range of tools, including Salesforce, Slack, and Notion, allowing businesses to easily integrate these platforms into existing workflows. Similarly, Claude integrates with AWS native services and open-source development frameworks, while Google’s Gemini runs on Google Cloud’s Vertex AI platform. While Gemini may require a slightly more complex setup, it is a great fit for teams already using Google’s infrastructure.

In contrast, open-source models—Mistral, Llama, and DeepSeek-R1—are not ready-to-use like cloud APIs. They require manual download and deployment, using libraries like Hugging Face Transformers, vLLM, or GGUF. This enables full control over the model’s functionality, data processing, and deployment environment. However, it requires skilled technical experts to maintain the infrastructure and manage updates. Additionally, there is no official vendor support, only open documentation and community forums.