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. The company has announced the release of its GPT-4.5 model, stating that it’s their largest and best model for chat yet.
Compared to its previous models, GPT-4.5 focuses on advancing unsupervised learning rather than chain-of-thought reasoning. Unlike reasoning-focused models such as o3 and DeepSeek R1, which use chain-of-thought processing to reason through complex problems methodically, GPT-4.5 responds based on its training data and pattern recognition capabilities. The model has a more general purpose than specialized reasoning models that excel at complex math, science, and logic problems.
Although the company has not yet disclosed the precise size or parameter count for GPT-4.5 at launch, its previous models, ChatGPT-4o and ChatGPT-4o mini, are believed to have more than 175 billion parameters, making them highly efficient at processing and generating large amounts of data. Both models have multimodal capabilities, allowing them to process both images and audio data.
Despite its advanced conversational and reasoning capabilities, 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 that 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 has been actively advancing its language model lineup, with Qwen2.5-Max released in early 2025, followed by the groundbreaking QwQ-32B in March. The QwQ model particularly stands out for its mathematical reasoning and coding capabilities, competing effectively with larger models like DeepSeek R1 while requiring significantly less computational resources.
Qwen2.5-Max is pretrained on over 20 trillion tokens and utilizes Mixture-of-Experts architecture for enhanced efficiency. While maintaining competitive performance across benchmarks, its design focuses on accessibility and practical deployment. The model features a 32K token context window, making it suitable for various enterprise applications.
For businesses and developers seeking comprehensive language models, the entire Qwen family spans from 1.8 billion to 72 billion parameters. All models are open-sourced under the Apache 2.0 license and available through multiple platforms including Alibaba Cloud API, Hugging Face, and ModelScope. The family has gained significant traction, with adoption by over 90,000 enterprises across consumer electronics, gaming, and other sectors.
Grok AI is a generative artificial intelligence chatbot developed by xAI, Elon Musk's AI company. Integrated with the social media platform X (formerly Twitter), Grok offers users real-time information access and a conversational experience infused with wit and humor. It is designed to handle a wide range of tasks, including answering questions, solving problems, brainstorming ideas, and generating images from text prompts.
The latest iteration, Grok 3, was launched in February 2025. This model was trained using ten times more computing power than its predecessor, Grok 2, utilizing xAI's Colossus supercomputer. Grok 3 introduces advanced reasoning capabilities, allowing it to break down complex problems into manageable steps and verify its solutions. It also features “Think” and “Big Brain” modes for enhanced problem-solving and a new “DeepSearch” function that scans the internet and X to provide detailed summaries in response to user queries.
Since this model excels in real-time data processing, advanced reasoning, and deep internet search, we'd recommend it to companies that would require fast news analysis, coding assistance, and dynamic customer support. Research-focused entities can benefit from its ability to monitor trends and analyze emerging issues in real-time.
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.
Anthropic unveiled its most advanced AI model to date, Claude 3.7 Sonnet, which integrates multiple reasoning approaches to provide users with the flexibility of rapid responses or in-depth, step-by-step problem-solving. The model’s standout feature is its “extended thinking mode,” leveraging a technique known as deliberate reasoning or self-reflection loops to allow the model to iteratively refine its thought process, evaluate multiple reasoning paths, and optimize for accuracy before finalizing an output.
Claude 3.7 Sonnet shows particularly strong improvements in coding and front-end web development, enabling more effective problem-solving in software engineering tasks. Its reasoning abilities are enhanced through the "extended thinking mode," which allows for deep reflection and refinement, leading to more accurate and reliable outputs. These strengths, coupled with capabilities in summarization, content generation, and conversational AI, make it an excellent choice for organizations looking for reliable AI in customer support, knowledge management, and business automation.
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.
Google has unveiled Gemini 2.5, the newest iteration of its AI reasoning model, designed to enhance complex problem-solving and multimodal understanding. This update significantly improves the model’s ability to process and generate text, images, and code, making it more efficient for real-world applications.
Gemini 2.5 offers several advanced features, including enhanced reasoning capabilities that excel in complex tasks. It has an impressive 1 million token context window, allowing it to process large documents seamlessly. The model is also highly capable in coding, able to generate fully functional applications and games from a single prompt. Gemini 2.5 Pro is multimodal, meaning it can handle text, images, and code, making it versatile for various content generation and analysis tasks. Additionally, it includes self-fact-checking features, helping to reduce inaccuracies and improve reliability.
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 3, supports context windows up to 128,000 tokens, facilitating long-form content generation and complex reasoning tasks. Available in sizes of 1 billion, 4 billion, 12 billion, and 27 billion parameters, Gemma 3 caters to diverse performance and resource requirements.
The latest Alibaba Qwen2.5-Max model is 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.hub
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.
🎉 Success! You're now signed up for the Shakudo newsletter.
Oops! Something went wrong while submitting the form.
Build with 175+ of the Best Data & AI Tools in One Place.
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. The company has announced the release of its GPT-4.5 model, stating that it’s their largest and best model for chat yet.
Compared to its previous models, GPT-4.5 focuses on advancing unsupervised learning rather than chain-of-thought reasoning. Unlike reasoning-focused models such as o3 and DeepSeek R1, which use chain-of-thought processing to reason through complex problems methodically, GPT-4.5 responds based on its training data and pattern recognition capabilities. The model has a more general purpose than specialized reasoning models that excel at complex math, science, and logic problems.
Although the company has not yet disclosed the precise size or parameter count for GPT-4.5 at launch, its previous models, ChatGPT-4o and ChatGPT-4o mini, are believed to have more than 175 billion parameters, making them highly efficient at processing and generating large amounts of data. Both models have multimodal capabilities, allowing them to process both images and audio data.
Despite its advanced conversational and reasoning capabilities, 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 that 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 has been actively advancing its language model lineup, with Qwen2.5-Max released in early 2025, followed by the groundbreaking QwQ-32B in March. The QwQ model particularly stands out for its mathematical reasoning and coding capabilities, competing effectively with larger models like DeepSeek R1 while requiring significantly less computational resources.
Qwen2.5-Max is pretrained on over 20 trillion tokens and utilizes Mixture-of-Experts architecture for enhanced efficiency. While maintaining competitive performance across benchmarks, its design focuses on accessibility and practical deployment. The model features a 32K token context window, making it suitable for various enterprise applications.
For businesses and developers seeking comprehensive language models, the entire Qwen family spans from 1.8 billion to 72 billion parameters. All models are open-sourced under the Apache 2.0 license and available through multiple platforms including Alibaba Cloud API, Hugging Face, and ModelScope. The family has gained significant traction, with adoption by over 90,000 enterprises across consumer electronics, gaming, and other sectors.
Grok AI is a generative artificial intelligence chatbot developed by xAI, Elon Musk's AI company. Integrated with the social media platform X (formerly Twitter), Grok offers users real-time information access and a conversational experience infused with wit and humor. It is designed to handle a wide range of tasks, including answering questions, solving problems, brainstorming ideas, and generating images from text prompts.
The latest iteration, Grok 3, was launched in February 2025. This model was trained using ten times more computing power than its predecessor, Grok 2, utilizing xAI's Colossus supercomputer. Grok 3 introduces advanced reasoning capabilities, allowing it to break down complex problems into manageable steps and verify its solutions. It also features “Think” and “Big Brain” modes for enhanced problem-solving and a new “DeepSearch” function that scans the internet and X to provide detailed summaries in response to user queries.
Since this model excels in real-time data processing, advanced reasoning, and deep internet search, we'd recommend it to companies that would require fast news analysis, coding assistance, and dynamic customer support. Research-focused entities can benefit from its ability to monitor trends and analyze emerging issues in real-time.
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.
Anthropic unveiled its most advanced AI model to date, Claude 3.7 Sonnet, which integrates multiple reasoning approaches to provide users with the flexibility of rapid responses or in-depth, step-by-step problem-solving. The model’s standout feature is its “extended thinking mode,” leveraging a technique known as deliberate reasoning or self-reflection loops to allow the model to iteratively refine its thought process, evaluate multiple reasoning paths, and optimize for accuracy before finalizing an output.
Claude 3.7 Sonnet shows particularly strong improvements in coding and front-end web development, enabling more effective problem-solving in software engineering tasks. Its reasoning abilities are enhanced through the "extended thinking mode," which allows for deep reflection and refinement, leading to more accurate and reliable outputs. These strengths, coupled with capabilities in summarization, content generation, and conversational AI, make it an excellent choice for organizations looking for reliable AI in customer support, knowledge management, and business automation.
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.
Google has unveiled Gemini 2.5, the newest iteration of its AI reasoning model, designed to enhance complex problem-solving and multimodal understanding. This update significantly improves the model’s ability to process and generate text, images, and code, making it more efficient for real-world applications.
Gemini 2.5 offers several advanced features, including enhanced reasoning capabilities that excel in complex tasks. It has an impressive 1 million token context window, allowing it to process large documents seamlessly. The model is also highly capable in coding, able to generate fully functional applications and games from a single prompt. Gemini 2.5 Pro is multimodal, meaning it can handle text, images, and code, making it versatile for various content generation and analysis tasks. Additionally, it includes self-fact-checking features, helping to reduce inaccuracies and improve reliability.
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 3, supports context windows up to 128,000 tokens, facilitating long-form content generation and complex reasoning tasks. Available in sizes of 1 billion, 4 billion, 12 billion, and 27 billion parameters, Gemma 3 caters to diverse performance and resource requirements.
The latest Alibaba Qwen2.5-Max model is 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.hub
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.
Get the whitepaper
Top 9 Large Language Models as of April 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. The company has announced the release of its GPT-4.5 model, stating that it’s their largest and best model for chat yet.
Compared to its previous models, GPT-4.5 focuses on advancing unsupervised learning rather than chain-of-thought reasoning. Unlike reasoning-focused models such as o3 and DeepSeek R1, which use chain-of-thought processing to reason through complex problems methodically, GPT-4.5 responds based on its training data and pattern recognition capabilities. The model has a more general purpose than specialized reasoning models that excel at complex math, science, and logic problems.
Although the company has not yet disclosed the precise size or parameter count for GPT-4.5 at launch, its previous models, ChatGPT-4o and ChatGPT-4o mini, are believed to have more than 175 billion parameters, making them highly efficient at processing and generating large amounts of data. Both models have multimodal capabilities, allowing them to process both images and audio data.
Despite its advanced conversational and reasoning capabilities, 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 that 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 has been actively advancing its language model lineup, with Qwen2.5-Max released in early 2025, followed by the groundbreaking QwQ-32B in March. The QwQ model particularly stands out for its mathematical reasoning and coding capabilities, competing effectively with larger models like DeepSeek R1 while requiring significantly less computational resources.
Qwen2.5-Max is pretrained on over 20 trillion tokens and utilizes Mixture-of-Experts architecture for enhanced efficiency. While maintaining competitive performance across benchmarks, its design focuses on accessibility and practical deployment. The model features a 32K token context window, making it suitable for various enterprise applications.
For businesses and developers seeking comprehensive language models, the entire Qwen family spans from 1.8 billion to 72 billion parameters. All models are open-sourced under the Apache 2.0 license and available through multiple platforms including Alibaba Cloud API, Hugging Face, and ModelScope. The family has gained significant traction, with adoption by over 90,000 enterprises across consumer electronics, gaming, and other sectors.
Grok AI is a generative artificial intelligence chatbot developed by xAI, Elon Musk's AI company. Integrated with the social media platform X (formerly Twitter), Grok offers users real-time information access and a conversational experience infused with wit and humor. It is designed to handle a wide range of tasks, including answering questions, solving problems, brainstorming ideas, and generating images from text prompts.
The latest iteration, Grok 3, was launched in February 2025. This model was trained using ten times more computing power than its predecessor, Grok 2, utilizing xAI's Colossus supercomputer. Grok 3 introduces advanced reasoning capabilities, allowing it to break down complex problems into manageable steps and verify its solutions. It also features “Think” and “Big Brain” modes for enhanced problem-solving and a new “DeepSearch” function that scans the internet and X to provide detailed summaries in response to user queries.
Since this model excels in real-time data processing, advanced reasoning, and deep internet search, we'd recommend it to companies that would require fast news analysis, coding assistance, and dynamic customer support. Research-focused entities can benefit from its ability to monitor trends and analyze emerging issues in real-time.
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.
Anthropic unveiled its most advanced AI model to date, Claude 3.7 Sonnet, which integrates multiple reasoning approaches to provide users with the flexibility of rapid responses or in-depth, step-by-step problem-solving. The model’s standout feature is its “extended thinking mode,” leveraging a technique known as deliberate reasoning or self-reflection loops to allow the model to iteratively refine its thought process, evaluate multiple reasoning paths, and optimize for accuracy before finalizing an output.
Claude 3.7 Sonnet shows particularly strong improvements in coding and front-end web development, enabling more effective problem-solving in software engineering tasks. Its reasoning abilities are enhanced through the "extended thinking mode," which allows for deep reflection and refinement, leading to more accurate and reliable outputs. These strengths, coupled with capabilities in summarization, content generation, and conversational AI, make it an excellent choice for organizations looking for reliable AI in customer support, knowledge management, and business automation.
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.
Google has unveiled Gemini 2.5, the newest iteration of its AI reasoning model, designed to enhance complex problem-solving and multimodal understanding. This update significantly improves the model’s ability to process and generate text, images, and code, making it more efficient for real-world applications.
Gemini 2.5 offers several advanced features, including enhanced reasoning capabilities that excel in complex tasks. It has an impressive 1 million token context window, allowing it to process large documents seamlessly. The model is also highly capable in coding, able to generate fully functional applications and games from a single prompt. Gemini 2.5 Pro is multimodal, meaning it can handle text, images, and code, making it versatile for various content generation and analysis tasks. Additionally, it includes self-fact-checking features, helping to reduce inaccuracies and improve reliability.
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 3, supports context windows up to 128,000 tokens, facilitating long-form content generation and complex reasoning tasks. Available in sizes of 1 billion, 4 billion, 12 billion, and 27 billion parameters, Gemma 3 caters to diverse performance and resource requirements.
The latest Alibaba Qwen2.5-Max model is 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.hub
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.
%20(1).avif)
.png){kind=icon}
%201.svg){kind=logo}
