If we had to choose one word to describe the rapid evolution of AI today, it would probably be something along the lines of explosive. As predicted by the Market Research Future report, the large language model (LLM) market in North America alone is expected to reach $105.5 billion by 2030. The exponential growth of AI tools combined with access to massive troves of text data has opened gates for better and more advanced content generation than we had ever hoped. Yet, such rapid expansion also makes it harder than ever to navigate and select the right tools among the diverse LLM models available.
The goal of this post is to keep you, the AI enthusiast and professional, up-to-date with current trends and essential innovations in the field. Below, we highlighted the top 9 LLMs that we think are currently making waves in the industry, each with distinct capabilities and specialized strengths, excelling in areas such as natural language processing, code synthesis, few-shot learning, or scalability. While we believe there is no one-size-fits-all LLM for every use case, we hope that this list can help you identify the most current and well-suited LLM model that meets your business’s unique requirements.
Our list kicks off with OpenAI's Generative Pre-trained Transformer (GPT) models, which have consistently exceeded their previous capabilities with each new release. Compared to its prior models, the latest ChatGPT-4o and ChatGPT-4o mini models offer significantly faster processing speeds and enhanced capabilities across text, voice, and vision.
The latest models are believed to have more than 175 billion parameters—surpassing the parameter count of ChatGPT-3, which had 175 billion—and a substantial context window of 128,000 tokens, making them highly efficient at processing and generating large amounts of data. Both of these models are equipped with multimodal capabilities to handle images as well as audio data.
Despite having advanced conversational and reasoning capabilities, note that GPT is a proprietary model, meaning that the training data and parameters are kept confidential by OpenAI, and access to full functionality is restricted–a commercial license or subscription is often required to unlock the complete range of features. In this case, we recommend this model for businesses looking to adopt an LLM that excels in conversational dialogue, multi-step reasoning, efficient computation, and real-time interactions without the constraints of a budget.
For companies who are curious to try out the proprietary models on the market before fully committing to one due to budget constraints or uncertainties about its long-term integration, Shakudo offers a compelling alternative. Our platform currently features a diverse selection of advanced LLMs with simplified deployment and scalability. With a simple subscription, you can access and assess the value of proprietary models, like GPT, before making a substantial investment.
With its latest R1 model, the Chinese AI company DeepSeek has once again set new benchmarks for innovation in the AI community. As of January 24th, the DeepSeek-R1 model is ranked fourth on Chatbot Arena, and top as the best open-source LM.
The DeepSeek-R1 is a 671B parameter Mixture-of-Experts (MoE) model with 37B activated parameters per token, trained through large-scale reinforcement learning with a strong focus on reasoning capabilities. The model excels at understanding and handling long-form content and demonstrates superior performance in complex tasks such as mathematics and code generation. The model is approximately 30 times more cost-efficient than OpenAI-o1 and 5 times faster, offering groundbreaking performance at a fraction of the cost. Moreover, it has shown exceptional precision in tasks requiring complex pattern recognition, such as genomic data analysis, medical imaging, and large-scale scientific simulations.
DeepSeek-R1’s capabilities are transformative when it comes to integration with proprietary enterprise data such as PII and financial records. Leveraging retrieval-augmented generation (RAG), enterprises can connect the model to their internal data sources to enable highly personalized, context-aware interactions—all while maintaining stringent security and compliance standards. With Shakudo, you can streamline the deployment and integration of advanced AI models like DeepSeek by automating the setup, deployment, and management processes. This eliminates the need for businesses to invest in and maintain extensive computing infrastructure. By operating within your existing infrastructure, the platform ensures seamless integration, enhanced security, and optimal performance without requiring significant in-house resources or specialized expertise.
Alibaba released Qwen2.5-Max earlier last month, the new model designed to deliver enhanced performance for large-scale natural language processing tasks. In instruct model evaluations, this new model outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also delivering strong performance in other assessments like MMLU-Pro.
Qwen2.5-Max is pretrained on over 20 trillion tokens. While specific details about its parameter count and token window size are not publicly disclosed, Qwen2.5-Max is designed for low-latency, high-efficiency tasks, making it suitable for applications requiring quick, accurate responses with low latency. Its smaller size allows for deployment on devices with limited computational resources.
For businesses and users looking for higher-performance models for natural language processing and AI-driven tasks, the Qwen 2.5 model is available on platforms like Hugging Face and ModelScope. This model boasts from 0.5 billion to 72 billion parameters, featuring context windows of up to 128,000 tokens, and is excellent for code generation, debugging, and automated forecasting.
EXAONE 3.0 is a bilingual LLM developed by LG AI Research. The company released its latest model in December 2024 with excellent performance across various benchmarks and real-world applications.
With 7.8 billion parameters, EXAONE 3.0 is capable of understanding and generating human-like text in multiple languages across complex domains, including coding, mathematics, patents, and chemistry. This model has also been optimized to reduce inference processing time by 56%, memory usage by 35%, and operating costs by 72%, ensuring that it remains cost-effective while maintaining high performance.
LG AI Research has open-sourced the 7.8B parameter instruction-tuned version of EXAONE 3.0 for non-commercial research purposes. This is a model we recommend to software companies or tech startups for generating Python code, assisting developers in troubleshooting, and creating APIs or other backend components.
Meta is still leading the front with their state-of-the-art LlaMa models. The company released its latest LlaMA 3.3 model in December 2024, featuring multimodal capabilities that can process both text and image for in-depth analysis and response generation, such as interpreting charts, maps, or translating texts identified in an image.
LlaMA 3.3 improves on previous models with a longer context window of up to 128,000 tokens and an optimized transformer architecture. With a parameter of 70 billion, this model outperforms open-source and proprietary alternatives in areas such as multilingual dialogue, reasoning, and coding.
Unlike ChatGPT models, LlaMA 3 is open-source, giving users the flexibility to access and deploy freely on their cloud depending on the specific requirements of their infrastructure, security preferences, or customization needs. We recommend this model to businesses looking for advanced content generation and language understanding, such as those in customer service, education, marketing, and consumer markets. The openness of these models also allows for your greater control over the model’s performance, tuning, and integration into existing workflows.
Next on our list is Claude, more specifically, the latest Claude 3.5 Sonnet model developed by Anthropic. We believe that Claude is arguably one of the most significant competitors to GPT since all of its current models–Claude 3 Haiku, Claude 3.5 Sonnet, and Claude 3 Opus–are designed with incredible contextual understanding capabilities that position themselves as the top conversational AI closely aligned with nuanced human interactions.
While the specific parameters of Claude 3.5 Sonnet remain undisclosed, the model boasts an impressive context window of 200,000 tokens, equivalent to approximately 150,000 words or 300 pages of text.
The current Claude subscription service is credit-based, and the cost can go as high as $2,304/month for enterprise plans tailored to high-volume users. We recommend Claude to mature or mid-stage businesses looking not only to adopt an AI that facilitates human-like interactions but also to enhance their coding capabilities since the Claude 3.5 Sonnet model is currently reaching a 49.0% performance score on the SWE-bench Verified benchmark, placing it as third among all publicly available models, including reasoning models and systems specifically designed for agentic coding.
Mistral's latest model – Mistral Small 3, a latency-optimized model was released under the Apache 2.0 license at the end of January. This 24-billion-parameter model is designed for low-latency, high-efficiency tasks. It processes approximately 150 tokens per second, making it over three times faster than Llama 3.3 70B on the same hardware.
This new model is ideal for applications requiring quick, accurate responses with low latency, such as virtual assistants, real-time data processing, and on-device command and control. Its smaller size allows for deployment on devices with limited computational resources.
Mistral Small 3 is currently open-source under the Apache 2.0 license. This means you can freely access and use the model for your own applications, provided you comply with the license terms. Since it is designed to be easily deployable, including on hardware with limited resources like a single GPU or even a MacBook with 32GB RAM, we'd recommend this to early-stage businesses looking to implement low-latency AI solutions without the need for extensive hardware infrastructure.
Gemini is a family of closed-source LLM models developed by Google. The latest model—Gemini 2.0 Flash—operates at twice the speed of Gemini 1.5 Pro, offering substantial improvements in speed, reasoning, and multimodal processing capabilities.
With that being said, Gemini remains a proprietary model; if your company deals with sensitive or confidential data regularly, you might be concerned about sending it to external servers due to security reasons. To address this concern, we recommend that you double-check vendor compliance regulations to ensure data privacy and security standards are met, such as adherence to GDPR, HIPAA, or other relevant data protection laws.
If you’re looking for an open-source alternative that exhibits capabilities almost as good as Gemini, Google’s latest Gemma model, Gemma 2, offers three models available in 2 billion, 9 billion, and 27 billion parameters with a context window of 8,200. For businesses looking for a rather economic option, this is the optimal choice that interprets and understands messages with remarkable accuracy.
Command R is a family of scalable models developed by Cohere with the goal of balancing high performance with strong accuracy, just like Claude. Both the Command R and Command R+ models offer APIs specifically optimized for Retrieval Augmented Generation (RAG). This means that these models can combine large-scale language generation with real-time information retrieval techniques for much more contextually aware outputs.
Currently, the Command R+ model boasts 104 billion parameters and offers an industry-leading 128,000 token context window for enhanced long-form processing and multi-turn conversation capabilities.
One of the perks of working with an open-source model is also to avoid vendor lock-in. Heavy reliance on a particular type of proprietary model may make it difficult for you to switch to alternative models when your business starts growing or the landscape changes. Cohere approaches this in a hybrid way, meaning that you can access and modify the model for personal usage but need a license for commercial use. In this case, we recommend this model for businesses that want flexibility in experimentation without a long-term commitment to a single vendor.
🎉 Success! You're now signed up for the Shakudo newsletter.
Oops! Something went wrong while submitting the form.
If we had to choose one word to describe the rapid evolution of AI today, it would probably be something along the lines of explosive. As predicted by the Market Research Future report, the large language model (LLM) market in North America alone is expected to reach $105.5 billion by 2030. The exponential growth of AI tools combined with access to massive troves of text data has opened gates for better and more advanced content generation than we had ever hoped. Yet, such rapid expansion also makes it harder than ever to navigate and select the right tools among the diverse LLM models available.
The goal of this post is to keep you, the AI enthusiast and professional, up-to-date with current trends and essential innovations in the field. Below, we highlighted the top 9 LLMs that we think are currently making waves in the industry, each with distinct capabilities and specialized strengths, excelling in areas such as natural language processing, code synthesis, few-shot learning, or scalability. While we believe there is no one-size-fits-all LLM for every use case, we hope that this list can help you identify the most current and well-suited LLM model that meets your business’s unique requirements.
Our list kicks off with OpenAI's Generative Pre-trained Transformer (GPT) models, which have consistently exceeded their previous capabilities with each new release. Compared to its prior models, the latest ChatGPT-4o and ChatGPT-4o mini models offer significantly faster processing speeds and enhanced capabilities across text, voice, and vision.
The latest models are believed to have more than 175 billion parameters—surpassing the parameter count of ChatGPT-3, which had 175 billion—and a substantial context window of 128,000 tokens, making them highly efficient at processing and generating large amounts of data. Both of these models are equipped with multimodal capabilities to handle images as well as audio data.
Despite having advanced conversational and reasoning capabilities, note that GPT is a proprietary model, meaning that the training data and parameters are kept confidential by OpenAI, and access to full functionality is restricted–a commercial license or subscription is often required to unlock the complete range of features. In this case, we recommend this model for businesses looking to adopt an LLM that excels in conversational dialogue, multi-step reasoning, efficient computation, and real-time interactions without the constraints of a budget.
For companies who are curious to try out the proprietary models on the market before fully committing to one due to budget constraints or uncertainties about its long-term integration, Shakudo offers a compelling alternative. Our platform currently features a diverse selection of advanced LLMs with simplified deployment and scalability. With a simple subscription, you can access and assess the value of proprietary models, like GPT, before making a substantial investment.
With its latest R1 model, the Chinese AI company DeepSeek has once again set new benchmarks for innovation in the AI community. As of January 24th, the DeepSeek-R1 model is ranked fourth on Chatbot Arena, and top as the best open-source LM.
The DeepSeek-R1 is a 671B parameter Mixture-of-Experts (MoE) model with 37B activated parameters per token, trained through large-scale reinforcement learning with a strong focus on reasoning capabilities. The model excels at understanding and handling long-form content and demonstrates superior performance in complex tasks such as mathematics and code generation. The model is approximately 30 times more cost-efficient than OpenAI-o1 and 5 times faster, offering groundbreaking performance at a fraction of the cost. Moreover, it has shown exceptional precision in tasks requiring complex pattern recognition, such as genomic data analysis, medical imaging, and large-scale scientific simulations.
DeepSeek-R1’s capabilities are transformative when it comes to integration with proprietary enterprise data such as PII and financial records. Leveraging retrieval-augmented generation (RAG), enterprises can connect the model to their internal data sources to enable highly personalized, context-aware interactions—all while maintaining stringent security and compliance standards. With Shakudo, you can streamline the deployment and integration of advanced AI models like DeepSeek by automating the setup, deployment, and management processes. This eliminates the need for businesses to invest in and maintain extensive computing infrastructure. By operating within your existing infrastructure, the platform ensures seamless integration, enhanced security, and optimal performance without requiring significant in-house resources or specialized expertise.
Alibaba released Qwen2.5-Max earlier last month, the new model designed to deliver enhanced performance for large-scale natural language processing tasks. In instruct model evaluations, this new model outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also delivering strong performance in other assessments like MMLU-Pro.
Qwen2.5-Max is pretrained on over 20 trillion tokens. While specific details about its parameter count and token window size are not publicly disclosed, Qwen2.5-Max is designed for low-latency, high-efficiency tasks, making it suitable for applications requiring quick, accurate responses with low latency. Its smaller size allows for deployment on devices with limited computational resources.
For businesses and users looking for higher-performance models for natural language processing and AI-driven tasks, the Qwen 2.5 model is available on platforms like Hugging Face and ModelScope. This model boasts from 0.5 billion to 72 billion parameters, featuring context windows of up to 128,000 tokens, and is excellent for code generation, debugging, and automated forecasting.
EXAONE 3.0 is a bilingual LLM developed by LG AI Research. The company released its latest model in December 2024 with excellent performance across various benchmarks and real-world applications.
With 7.8 billion parameters, EXAONE 3.0 is capable of understanding and generating human-like text in multiple languages across complex domains, including coding, mathematics, patents, and chemistry. This model has also been optimized to reduce inference processing time by 56%, memory usage by 35%, and operating costs by 72%, ensuring that it remains cost-effective while maintaining high performance.
LG AI Research has open-sourced the 7.8B parameter instruction-tuned version of EXAONE 3.0 for non-commercial research purposes. This is a model we recommend to software companies or tech startups for generating Python code, assisting developers in troubleshooting, and creating APIs or other backend components.
Meta is still leading the front with their state-of-the-art LlaMa models. The company released its latest LlaMA 3.3 model in December 2024, featuring multimodal capabilities that can process both text and image for in-depth analysis and response generation, such as interpreting charts, maps, or translating texts identified in an image.
LlaMA 3.3 improves on previous models with a longer context window of up to 128,000 tokens and an optimized transformer architecture. With a parameter of 70 billion, this model outperforms open-source and proprietary alternatives in areas such as multilingual dialogue, reasoning, and coding.
Unlike ChatGPT models, LlaMA 3 is open-source, giving users the flexibility to access and deploy freely on their cloud depending on the specific requirements of their infrastructure, security preferences, or customization needs. We recommend this model to businesses looking for advanced content generation and language understanding, such as those in customer service, education, marketing, and consumer markets. The openness of these models also allows for your greater control over the model’s performance, tuning, and integration into existing workflows.
Next on our list is Claude, more specifically, the latest Claude 3.5 Sonnet model developed by Anthropic. We believe that Claude is arguably one of the most significant competitors to GPT since all of its current models–Claude 3 Haiku, Claude 3.5 Sonnet, and Claude 3 Opus–are designed with incredible contextual understanding capabilities that position themselves as the top conversational AI closely aligned with nuanced human interactions.
While the specific parameters of Claude 3.5 Sonnet remain undisclosed, the model boasts an impressive context window of 200,000 tokens, equivalent to approximately 150,000 words or 300 pages of text.
The current Claude subscription service is credit-based, and the cost can go as high as $2,304/month for enterprise plans tailored to high-volume users. We recommend Claude to mature or mid-stage businesses looking not only to adopt an AI that facilitates human-like interactions but also to enhance their coding capabilities since the Claude 3.5 Sonnet model is currently reaching a 49.0% performance score on the SWE-bench Verified benchmark, placing it as third among all publicly available models, including reasoning models and systems specifically designed for agentic coding.
Mistral's latest model – Mistral Small 3, a latency-optimized model was released under the Apache 2.0 license at the end of January. This 24-billion-parameter model is designed for low-latency, high-efficiency tasks. It processes approximately 150 tokens per second, making it over three times faster than Llama 3.3 70B on the same hardware.
This new model is ideal for applications requiring quick, accurate responses with low latency, such as virtual assistants, real-time data processing, and on-device command and control. Its smaller size allows for deployment on devices with limited computational resources.
Mistral Small 3 is currently open-source under the Apache 2.0 license. This means you can freely access and use the model for your own applications, provided you comply with the license terms. Since it is designed to be easily deployable, including on hardware with limited resources like a single GPU or even a MacBook with 32GB RAM, we'd recommend this to early-stage businesses looking to implement low-latency AI solutions without the need for extensive hardware infrastructure.
Gemini is a family of closed-source LLM models developed by Google. The latest model—Gemini 2.0 Flash—operates at twice the speed of Gemini 1.5 Pro, offering substantial improvements in speed, reasoning, and multimodal processing capabilities.
With that being said, Gemini remains a proprietary model; if your company deals with sensitive or confidential data regularly, you might be concerned about sending it to external servers due to security reasons. To address this concern, we recommend that you double-check vendor compliance regulations to ensure data privacy and security standards are met, such as adherence to GDPR, HIPAA, or other relevant data protection laws.
If you’re looking for an open-source alternative that exhibits capabilities almost as good as Gemini, Google’s latest Gemma model, Gemma 2, offers three models available in 2 billion, 9 billion, and 27 billion parameters with a context window of 8,200. For businesses looking for a rather economic option, this is the optimal choice that interprets and understands messages with remarkable accuracy.
Command R is a family of scalable models developed by Cohere with the goal of balancing high performance with strong accuracy, just like Claude. Both the Command R and Command R+ models offer APIs specifically optimized for Retrieval Augmented Generation (RAG). This means that these models can combine large-scale language generation with real-time information retrieval techniques for much more contextually aware outputs.
Currently, the Command R+ model boasts 104 billion parameters and offers an industry-leading 128,000 token context window for enhanced long-form processing and multi-turn conversation capabilities.
One of the perks of working with an open-source model is also to avoid vendor lock-in. Heavy reliance on a particular type of proprietary model may make it difficult for you to switch to alternative models when your business starts growing or the landscape changes. Cohere approaches this in a hybrid way, meaning that you can access and modify the model for personal usage but need a license for commercial use. In this case, we recommend this model for businesses that want flexibility in experimentation without a long-term commitment to a single vendor.
Get the whitepaper
Top 9 Large Language Models as of Feburary 2025
Thank you for filling out the form. The whitepaper you have requested is available for download below.
If we had to choose one word to describe the rapid evolution of AI today, it would probably be something along the lines of explosive. As predicted by the Market Research Future report, the large language model (LLM) market in North America alone is expected to reach $105.5 billion by 2030. The exponential growth of AI tools combined with access to massive troves of text data has opened gates for better and more advanced content generation than we had ever hoped. Yet, such rapid expansion also makes it harder than ever to navigate and select the right tools among the diverse LLM models available.
The goal of this post is to keep you, the AI enthusiast and professional, up-to-date with current trends and essential innovations in the field. Below, we highlighted the top 9 LLMs that we think are currently making waves in the industry, each with distinct capabilities and specialized strengths, excelling in areas such as natural language processing, code synthesis, few-shot learning, or scalability. While we believe there is no one-size-fits-all LLM for every use case, we hope that this list can help you identify the most current and well-suited LLM model that meets your business’s unique requirements.
Our list kicks off with OpenAI's Generative Pre-trained Transformer (GPT) models, which have consistently exceeded their previous capabilities with each new release. Compared to its prior models, the latest ChatGPT-4o and ChatGPT-4o mini models offer significantly faster processing speeds and enhanced capabilities across text, voice, and vision.
The latest models are believed to have more than 175 billion parameters—surpassing the parameter count of ChatGPT-3, which had 175 billion—and a substantial context window of 128,000 tokens, making them highly efficient at processing and generating large amounts of data. Both of these models are equipped with multimodal capabilities to handle images as well as audio data.
Despite having advanced conversational and reasoning capabilities, note that GPT is a proprietary model, meaning that the training data and parameters are kept confidential by OpenAI, and access to full functionality is restricted–a commercial license or subscription is often required to unlock the complete range of features. In this case, we recommend this model for businesses looking to adopt an LLM that excels in conversational dialogue, multi-step reasoning, efficient computation, and real-time interactions without the constraints of a budget.
For companies who are curious to try out the proprietary models on the market before fully committing to one due to budget constraints or uncertainties about its long-term integration, Shakudo offers a compelling alternative. Our platform currently features a diverse selection of advanced LLMs with simplified deployment and scalability. With a simple subscription, you can access and assess the value of proprietary models, like GPT, before making a substantial investment.
With its latest R1 model, the Chinese AI company DeepSeek has once again set new benchmarks for innovation in the AI community. As of January 24th, the DeepSeek-R1 model is ranked fourth on Chatbot Arena, and top as the best open-source LM.
The DeepSeek-R1 is a 671B parameter Mixture-of-Experts (MoE) model with 37B activated parameters per token, trained through large-scale reinforcement learning with a strong focus on reasoning capabilities. The model excels at understanding and handling long-form content and demonstrates superior performance in complex tasks such as mathematics and code generation. The model is approximately 30 times more cost-efficient than OpenAI-o1 and 5 times faster, offering groundbreaking performance at a fraction of the cost. Moreover, it has shown exceptional precision in tasks requiring complex pattern recognition, such as genomic data analysis, medical imaging, and large-scale scientific simulations.
DeepSeek-R1’s capabilities are transformative when it comes to integration with proprietary enterprise data such as PII and financial records. Leveraging retrieval-augmented generation (RAG), enterprises can connect the model to their internal data sources to enable highly personalized, context-aware interactions—all while maintaining stringent security and compliance standards. With Shakudo, you can streamline the deployment and integration of advanced AI models like DeepSeek by automating the setup, deployment, and management processes. This eliminates the need for businesses to invest in and maintain extensive computing infrastructure. By operating within your existing infrastructure, the platform ensures seamless integration, enhanced security, and optimal performance without requiring significant in-house resources or specialized expertise.
Alibaba released Qwen2.5-Max earlier last month, the new model designed to deliver enhanced performance for large-scale natural language processing tasks. In instruct model evaluations, this new model outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also delivering strong performance in other assessments like MMLU-Pro.
Qwen2.5-Max is pretrained on over 20 trillion tokens. While specific details about its parameter count and token window size are not publicly disclosed, Qwen2.5-Max is designed for low-latency, high-efficiency tasks, making it suitable for applications requiring quick, accurate responses with low latency. Its smaller size allows for deployment on devices with limited computational resources.
For businesses and users looking for higher-performance models for natural language processing and AI-driven tasks, the Qwen 2.5 model is available on platforms like Hugging Face and ModelScope. This model boasts from 0.5 billion to 72 billion parameters, featuring context windows of up to 128,000 tokens, and is excellent for code generation, debugging, and automated forecasting.
EXAONE 3.0 is a bilingual LLM developed by LG AI Research. The company released its latest model in December 2024 with excellent performance across various benchmarks and real-world applications.
With 7.8 billion parameters, EXAONE 3.0 is capable of understanding and generating human-like text in multiple languages across complex domains, including coding, mathematics, patents, and chemistry. This model has also been optimized to reduce inference processing time by 56%, memory usage by 35%, and operating costs by 72%, ensuring that it remains cost-effective while maintaining high performance.
LG AI Research has open-sourced the 7.8B parameter instruction-tuned version of EXAONE 3.0 for non-commercial research purposes. This is a model we recommend to software companies or tech startups for generating Python code, assisting developers in troubleshooting, and creating APIs or other backend components.
Meta is still leading the front with their state-of-the-art LlaMa models. The company released its latest LlaMA 3.3 model in December 2024, featuring multimodal capabilities that can process both text and image for in-depth analysis and response generation, such as interpreting charts, maps, or translating texts identified in an image.
LlaMA 3.3 improves on previous models with a longer context window of up to 128,000 tokens and an optimized transformer architecture. With a parameter of 70 billion, this model outperforms open-source and proprietary alternatives in areas such as multilingual dialogue, reasoning, and coding.
Unlike ChatGPT models, LlaMA 3 is open-source, giving users the flexibility to access and deploy freely on their cloud depending on the specific requirements of their infrastructure, security preferences, or customization needs. We recommend this model to businesses looking for advanced content generation and language understanding, such as those in customer service, education, marketing, and consumer markets. The openness of these models also allows for your greater control over the model’s performance, tuning, and integration into existing workflows.
Next on our list is Claude, more specifically, the latest Claude 3.5 Sonnet model developed by Anthropic. We believe that Claude is arguably one of the most significant competitors to GPT since all of its current models–Claude 3 Haiku, Claude 3.5 Sonnet, and Claude 3 Opus–are designed with incredible contextual understanding capabilities that position themselves as the top conversational AI closely aligned with nuanced human interactions.
While the specific parameters of Claude 3.5 Sonnet remain undisclosed, the model boasts an impressive context window of 200,000 tokens, equivalent to approximately 150,000 words or 300 pages of text.
The current Claude subscription service is credit-based, and the cost can go as high as $2,304/month for enterprise plans tailored to high-volume users. We recommend Claude to mature or mid-stage businesses looking not only to adopt an AI that facilitates human-like interactions but also to enhance their coding capabilities since the Claude 3.5 Sonnet model is currently reaching a 49.0% performance score on the SWE-bench Verified benchmark, placing it as third among all publicly available models, including reasoning models and systems specifically designed for agentic coding.
Mistral's latest model – Mistral Small 3, a latency-optimized model was released under the Apache 2.0 license at the end of January. This 24-billion-parameter model is designed for low-latency, high-efficiency tasks. It processes approximately 150 tokens per second, making it over three times faster than Llama 3.3 70B on the same hardware.
This new model is ideal for applications requiring quick, accurate responses with low latency, such as virtual assistants, real-time data processing, and on-device command and control. Its smaller size allows for deployment on devices with limited computational resources.
Mistral Small 3 is currently open-source under the Apache 2.0 license. This means you can freely access and use the model for your own applications, provided you comply with the license terms. Since it is designed to be easily deployable, including on hardware with limited resources like a single GPU or even a MacBook with 32GB RAM, we'd recommend this to early-stage businesses looking to implement low-latency AI solutions without the need for extensive hardware infrastructure.
Gemini is a family of closed-source LLM models developed by Google. The latest model—Gemini 2.0 Flash—operates at twice the speed of Gemini 1.5 Pro, offering substantial improvements in speed, reasoning, and multimodal processing capabilities.
With that being said, Gemini remains a proprietary model; if your company deals with sensitive or confidential data regularly, you might be concerned about sending it to external servers due to security reasons. To address this concern, we recommend that you double-check vendor compliance regulations to ensure data privacy and security standards are met, such as adherence to GDPR, HIPAA, or other relevant data protection laws.
If you’re looking for an open-source alternative that exhibits capabilities almost as good as Gemini, Google’s latest Gemma model, Gemma 2, offers three models available in 2 billion, 9 billion, and 27 billion parameters with a context window of 8,200. For businesses looking for a rather economic option, this is the optimal choice that interprets and understands messages with remarkable accuracy.
Command R is a family of scalable models developed by Cohere with the goal of balancing high performance with strong accuracy, just like Claude. Both the Command R and Command R+ models offer APIs specifically optimized for Retrieval Augmented Generation (RAG). This means that these models can combine large-scale language generation with real-time information retrieval techniques for much more contextually aware outputs.
Currently, the Command R+ model boasts 104 billion parameters and offers an industry-leading 128,000 token context window for enhanced long-form processing and multi-turn conversation capabilities.
One of the perks of working with an open-source model is also to avoid vendor lock-in. Heavy reliance on a particular type of proprietary model may make it difficult for you to switch to alternative models when your business starts growing or the landscape changes. Cohere approaches this in a hybrid way, meaning that you can access and modify the model for personal usage but need a license for commercial use. In this case, we recommend this model for businesses that want flexibility in experimentation without a long-term commitment to a single vendor.
.png){kind=icon}
%201.svg){kind=logo}
