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Introduction
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.
The Chinese AI company DeepSeek has recently unveiled its DeepSeek-V3 model, which has shown impressive performance across various benchmarks. This model has showcased strong performance across multiple benchmarks, matching models like Qwen 2.5 72B and outperforming both GPT-4o and Mistral Large models.
DeepSeek-V3 boasts an impressive 671 billion parameters, positioning it among the largest models in the AI landscape. With a context window of up to 128,000 tokens and a Multi-Head Latent Attention mechanism, it excels at processing complex tasks with exceptional accuracy and coherence. DeepSeek-V3 is also trained on 14.8 trillion high-quality tokens, making it incredibly versatile across different domains and applications.
DeepSeek is exceptionally good at understanding and handling long-form content, making it ideal for businesses with large data processing requirements. It also demonstrated superior performance in tasks such as mathematics and code generation. As an open-source model, we recommend this model to companies with limited budgets seeking customizable solutions.
Alibaba’s most recent release of QWQ made waves in the AI community. QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities.
With 32.5 billion parameters, the latest model outperforms some existing models in these areas, offering the ability to handle complex tasks. Even as a preview model, it has demonstrated significant strengths in areas like coding and analytical tasks, such as mathematical computations and logical deductions. It achieved a much higher result in mathematics and AIME performance, surpassing OpenAI's o1-preview and GPT-4 models.
The model is currently available for testing on platforms like Hugging Face, but full access is limited. Its unique approach to reasoning allows it to verify its answers through planning and self-checking. This is a model we’d recommend to businesses looking to process large volumes of data with sophisticated reasoning and logical insights.
For businesses and users looking for an open-source alternative, 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 model LlaMA 3.2 in September 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 3.2 includes powerful models with 8, 70, and 405 billion parameters, providing a versatile range for different use cases. With a context window of 128,000 tokens, it can handle vast and complex data inputs at a time.
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 Large 2 is coming out with outstanding capabilities, particularly when it comes to computational efficiency, coding support, and safety features.
The model carries 123 billion parameters and a massive 128,000 token context window, meaning that it’s capable of holding coherence across long passages of text, making it ideal for complex applications requiring large volumes of document processing.
While Mistral Large 2 is not entirely open-source, the company makes it easily accessible on platforms such as Hugging Face, so that other businesses can download them for deployment in their own environment. However, compared to open-source models, you may find it difficult to fine-tune and customize this one for specific applications.
Gemini is a family of closed-source LLM models dqeveloped by Google; current models–Gemini 1.0 Nano, Gemini 1.5 Flash, Gemini 1.5 Pro, and Gemini 1.0 Ultra—are designed to operate on different devices, from smartphones to heavy servers. With an incredible 1.5 trillion parameters, Gemini is one of the largest and most advanced language models developed to date.
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.
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.
The Chinese AI company DeepSeek has recently unveiled its DeepSeek-V3 model, which has shown impressive performance across various benchmarks. This model has showcased strong performance across multiple benchmarks, matching models like Qwen 2.5 72B and outperforming both GPT-4o and Mistral Large models.
DeepSeek-V3 boasts an impressive 671 billion parameters, positioning it among the largest models in the AI landscape. With a context window of up to 128,000 tokens and a Multi-Head Latent Attention mechanism, it excels at processing complex tasks with exceptional accuracy and coherence. DeepSeek-V3 is also trained on 14.8 trillion high-quality tokens, making it incredibly versatile across different domains and applications.
DeepSeek is exceptionally good at understanding and handling long-form content, making it ideal for businesses with large data processing requirements. It also demonstrated superior performance in tasks such as mathematics and code generation. As an open-source model, we recommend this model to companies with limited budgets seeking customizable solutions.
Alibaba’s most recent release of QWQ made waves in the AI community. QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities.
With 32.5 billion parameters, the latest model outperforms some existing models in these areas, offering the ability to handle complex tasks. Even as a preview model, it has demonstrated significant strengths in areas like coding and analytical tasks, such as mathematical computations and logical deductions. It achieved a much higher result in mathematics and AIME performance, surpassing OpenAI's o1-preview and GPT-4 models.
The model is currently available for testing on platforms like Hugging Face, but full access is limited. Its unique approach to reasoning allows it to verify its answers through planning and self-checking. This is a model we’d recommend to businesses looking to process large volumes of data with sophisticated reasoning and logical insights.
For businesses and users looking for an open-source alternative, 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 model LlaMA 3.2 in September 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 3.2 includes powerful models with 8, 70, and 405 billion parameters, providing a versatile range for different use cases. With a context window of 128,000 tokens, it can handle vast and complex data inputs at a time.
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 Large 2 is coming out with outstanding capabilities, particularly when it comes to computational efficiency, coding support, and safety features.
The model carries 123 billion parameters and a massive 128,000 token context window, meaning that it’s capable of holding coherence across long passages of text, making it ideal for complex applications requiring large volumes of document processing.
While Mistral Large 2 is not entirely open-source, the company makes it easily accessible on platforms such as Hugging Face, so that other businesses can download them for deployment in their own environment. However, compared to open-source models, you may find it difficult to fine-tune and customize this one for specific applications.
Gemini is a family of closed-source LLM models dqeveloped by Google; current models–Gemini 1.0 Nano, Gemini 1.5 Flash, Gemini 1.5 Pro, and Gemini 1.0 Ultra—are designed to operate on different devices, from smartphones to heavy servers. With an incredible 1.5 trillion parameters, Gemini is one of the largest and most advanced language models developed to date.
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.
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Top 9 Large Language Models as of January 2025
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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.
The Chinese AI company DeepSeek has recently unveiled its DeepSeek-V3 model, which has shown impressive performance across various benchmarks. This model has showcased strong performance across multiple benchmarks, matching models like Qwen 2.5 72B and outperforming both GPT-4o and Mistral Large models.
DeepSeek-V3 boasts an impressive 671 billion parameters, positioning it among the largest models in the AI landscape. With a context window of up to 128,000 tokens and a Multi-Head Latent Attention mechanism, it excels at processing complex tasks with exceptional accuracy and coherence. DeepSeek-V3 is also trained on 14.8 trillion high-quality tokens, making it incredibly versatile across different domains and applications.
DeepSeek is exceptionally good at understanding and handling long-form content, making it ideal for businesses with large data processing requirements. It also demonstrated superior performance in tasks such as mathematics and code generation. As an open-source model, we recommend this model to companies with limited budgets seeking customizable solutions.
Alibaba’s most recent release of QWQ made waves in the AI community. QwQ-32B-Preview is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities.
With 32.5 billion parameters, the latest model outperforms some existing models in these areas, offering the ability to handle complex tasks. Even as a preview model, it has demonstrated significant strengths in areas like coding and analytical tasks, such as mathematical computations and logical deductions. It achieved a much higher result in mathematics and AIME performance, surpassing OpenAI's o1-preview and GPT-4 models.
The model is currently available for testing on platforms like Hugging Face, but full access is limited. Its unique approach to reasoning allows it to verify its answers through planning and self-checking. This is a model we’d recommend to businesses looking to process large volumes of data with sophisticated reasoning and logical insights.
For businesses and users looking for an open-source alternative, 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 model LlaMA 3.2 in September 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 3.2 includes powerful models with 8, 70, and 405 billion parameters, providing a versatile range for different use cases. With a context window of 128,000 tokens, it can handle vast and complex data inputs at a time.
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 Large 2 is coming out with outstanding capabilities, particularly when it comes to computational efficiency, coding support, and safety features.
The model carries 123 billion parameters and a massive 128,000 token context window, meaning that it’s capable of holding coherence across long passages of text, making it ideal for complex applications requiring large volumes of document processing.
While Mistral Large 2 is not entirely open-source, the company makes it easily accessible on platforms such as Hugging Face, so that other businesses can download them for deployment in their own environment. However, compared to open-source models, you may find it difficult to fine-tune and customize this one for specific applications.
Gemini is a family of closed-source LLM models dqeveloped by Google; current models–Gemini 1.0 Nano, Gemini 1.5 Flash, Gemini 1.5 Pro, and Gemini 1.0 Ultra—are designed to operate on different devices, from smartphones to heavy servers. With an incredible 1.5 trillion parameters, Gemini is one of the largest and most advanced language models developed to date.
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.
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