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LLM сomparison: How to choose the right model for your business
Major companies like Spotify, Salesforce, and Duolingo are already using large language models (LLMs) to improve podcast recommendations, facilitate financial analytics, and deliver hyper-personalized experiences to global users. Following their lead, more businesses have begun integrating LLMs into their operations. But with an impressive list of large language models, choosing the right one becomes a challenge.
In this LLM model comparison, we outline key factors to consider when choosing a solution, such as performance, cost-efficiency, security, scalability, and more.
Quick LLM market overview
Although the Transformer architecture, which powers modern LLM models, has existed since 2017, the real boom in LLM adoption began with the release of Open AI’s GPT-3.5 in late 2022. Since then, the variety of solutions available, the capabilities of LLMs, and the size of the market have all grown exponentially. In 2024, the global LLM market was valued at $6.4 billion and is expected to reach $36.1 billion by 2030, at a compound annual growth rate (CAGR) of 33.2%.
The speed of innovation—major releases are now happening every few months—makes the LLM market particularly complex. Between 2023 and early 2025 alone, OpenAI launched GPT-4, GPT-4o with 4o-mini, o1 with o1-mini, and GPT-4.5; Anthropic released three generations of Claude family; Google launched three generations of Gemini; and Meta released multiple iterations of Llama 2 and 3. Newcomers like Mistral and DeepSeek entered the market in mid-2023 and introduced high-performance models within months.
These vendors compete not only for accuracy but also for latency, cost, security, and customization flexibility. As a result, businesses must conduct thorough assessments to make informed, timely decisions.
Performance and scalability
When conducting an LLM benchmark comparison, some look at model size and number of parameters. However, in this case, larger doesn’t mean better. What truly matters is whether an LLM can handle large workloads without quality degradation or latency, how well the model integrates into your infrastructure, and how efficiently it processes the file types you work with. Therefore, the key metrics to meaningfully compare LLMs are throughput, context window, deployment format, and multimodal capabilities.
Throughput
Throughput determines a model’s responsiveness, measuring how many tokens an LLM can generate per second. This is particularly important for real-time apps and chatbots. Among the fastest models available are o3-mini (188 tokens/sec), Gemini 2.0 Flash (254 tokens/sec), Llama 3.2 1B (265 tokens/sec), and Ministal 3B (220 tokens/sec).
Context window
Having a large context window means the model can see more information at once, which is better for complex reasoning, summarization, and document analysis. The leaders in this benchmark are Gemini 2.0 Flash and Flash-Lite with a 1M token context window, followed by the Claude family with 200K (Claude Sonnet can reach 1M in pro use cases), as well as o1 and o3-mini with 200K.
Multimodal capabilities
Models that process multiple input/output types are more versatile. GPT-4o leads this category with full multimodal support (text, image, audio, and video). Gemini 2.0 Flash can also handle text, image, audio, and video at the input level, but generates output in text-only format. As for coding-specific needs, Codestral stands out, supporting input/output in the code format.
Deployment format
Cloud solutions, such as GPT-series, o1, o3, Gemini, and Claude, are more flexible and easier to integrate. Deploying these solutions requires no investment in specific hardware. Additionally, providers regularly update cloud LLMs, offering users the most relevant features. However, for businesses with strict data protection requirements, open-source LLMs are a better fit. Models like Llama, Codestral, Mistral, Pixtral, and Ministral run entirely on an organization’s own hardware, ensuring no data is transmitted via the internet and giving businesses full control over security settings and updates.
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Explore our expertiseData 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.
We developed and integrated a voice assistant powered by generative AI (GenAI) agents connected to the client’s automotive infotainment system, streamlining interactions for Tesla drivers and increasing safety.
LLM pricing
Most providers break down pricing into:
- Input tokens: What the model reads
- Output tokens: What the model generates in response
- Cached tokens: Reused prompts (some providers charge less for a stored and reused input)
Understanding this policy is crucial when assessing the cost of using cloud-based LLMs. Below, we outline the pricing for leading cloud-only models as of April 2025.
| Input price (per 1M tokens) | Cached input price (per 1M tokens) | Output price (per 1M tokens) | |
|---|---|---|---|
| GPT-4o | $2.50 | $1.25 | $10.00 |
| GPT-4o mini | $0.15 | $0.08 | $0.60 |
| o1 | $15.00 | $7.50 | $60.00 |
| o3-mini | $1.10 | $0.55 | $4.40 |
| Gemini 2.0 Flash | $0.10 for text, image, and video or $0.70 for audio | $0.025 for text, image, and video or $0.175 for audio | $0.40 |
| Gemini 2.0 Flash-Lite | $0.08 | TBD | $0.30 |
| Claude Haiku | $0.80 | $1.00 for prompt caching write and $0.08 for prompt caching read | $4.00 |
| Claude Sonnet | $3.00 | $3.75 for prompt caching write and $0.30 for prompt caching read | $15.00 |
| Claude Opus | $15.00 | $18.75 for prompt caching write and $1.50 for prompt caching read | $75.00 |
| DeepSeek-R1 | $0.14 | $0.55 | $2.19 |
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.
A thorough analysis of a credit management company’s operations allowed us to map the AI opportunities capable of improving staff productivity and the customer experience, ultimately driving growth.
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.
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See how we can helpConclusion
Today’s market offers a wide range of LLMs, and our analysis shows that there is no one-size-fits-all solution. The right choice depends on what you want to achieve.
If you are looking for high-performance, general-purpose models with strong enterprise support and fast integration, consider cloud-based options like OpenAI’s GPT series, Anthropic’s Claude, or Google’s Gemini. When cost is key, Gemini Flash, Claude Haiku, or Mistral 3B are great options. If you operate in a sensitive industry like finance or healthcare, the best fit may be an open-source model like Llama, Mistral, or Pixtral.
Still unsure which model to pick? Our certified AI engineers can help you. At EffectiveSoft, we adjust LLMs for your specific use case. Just drop us a line!
FAQ about LLM comparison
LLMs are advanced neural networks that understand and generate natural language. Built on deep learning, they analyze the vast amount of text available on the internet—books, articles, web pages, and beyond—to identify patterns and generate appropriate responses.
A token is a set of words, characters, or combination of words and characters that LLMs process when decomposing text.
Theoretically, yes. But this requires massive compute resources that may cost millions of dollars, access to high-quality datasets, and more. Fine-tuning and adapting existing models is more efficient and cost-effective.
You may need hundreds to thousands of examples for an LLM to learn new task-specific patterns. It depends on the depth of customization you need. The larger the datasets, the better the model’s performance.
An accurate answer depends on the model size, data volume, and fine-tuning method. Contact us for a consultation.
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