Top 40 Large Language Models (LLMs) in 2025: The Definitive Guide

As artificial intelligence continues to evolve, large language models (LLMs) have become integral to various applications, from content creation to customer service. In 2025, the landscape of LLMs is more diverse and powerful than ever. This guide provides an in-depth look at the top 40 LLMs that are shaping the AI industry today.

What are Large Language Models (LLMs)?

Large Language Models (LLMs) are a type of artificial intelligence (AI) model that is trained on vast amounts of text data to understand and generate human language. These models are based on neural networks, particularly a class of models called transformers, which are designed to process and generate sequences of words in a way that mimics human language.

Key Characteristics of LLMs:

1. Scale: LLMs are characterized by their massive size, typically having billions or even trillions of parameters (the weights within the model that help it learn patterns). For example, GPT-3 has 175 billion parameters, and newer models like GPT-4 have even more.

2. Training: These models are trained on diverse datasets that include books, articles, websites, and other written material, allowing them to learn language patterns, grammar, context, and world knowledge.

3. Contextual Understanding: LLMs can generate text based on the context provided by a prompt. They can understand and respond to questions, write essays, summarize documents, translate languages, and more, by predicting the most likely sequence of words based on what they’ve learned.

4. Generative Abilities: LLMs don’t just analyze text—they can generate coherent and contextually relevant responses or content based on prompts. This makes them useful for tasks such as chatbots, content creation, and language translation.

5. Applications:

   • Natural Language Processing (NLP) tasks such as translation, summarization, question-answering, and text classification.

   • Conversational AI, including virtual assistants and chatbots.

   • Text Generation, such as writing essays, articles, or even creative content.

   • Code Generation, where models like OpenAI’s Codex generate code snippets.

   • Knowledge Extraction, helping systems retrieve and summarize information from large text corpora.

Top 40 Large Language Models (LLMs) in 2025

Model Name	Organization	Size	Release Year	Key Features	Open Source	Context Window (Tokens)
GPT-4.5	OpenAI	Unknown	2024	Faster than GPT-4, improved reasoning	No	128,000
Claude 3.7 Sonnet	Anthropic	Unknown	2025	Emphasizes safety and reliability	No	200,000
Gemini 2.5 Pro	Google	Unknown	2025	Multimodal, Workspace-integrated, fast	No	1,000,000
LLaMA 4	Meta	Unknown	2025	Efficient, open-weights, multilingual	Yes	10,000,000
Grok-3	xAI (Elon Musk)	Unknown	2025	Deeply integrated with X, humorous, real-time awareness	No	128,000
DeepSeek R1	DeepSeek	Unknown	2024	Emphasizes planning, self-reflection, and evolution	Yes	128,000
Qwen 3	Alibaba	Unknown	2024	Strong multilingual, aligned for instructions	Yes	128,000
Gemma 3	Google	Small/Med (local)	2025	Designed for on-device and server deployment	Yes	128,000
Command R+	Cohere	Unknown	2024	Retrieval-augmented generation (RAG), strong performance	No	128,000
Mistral Large-Instruct-2407	Mistral	Unknown	2024	Instruction-tuned, strong multilingual reasoning	Yes	32,000
Collective-1	Flower AI Collective	Unknown	2025	Community-trained, privacy-respecting, distributed model training	Yes	128,000
NeoBERT	Open Source Collective	Small/Medium	2024	Lightweight transformer-based BERT variant, NLP-tuned	Unknown	4,096
GPT-J	EleutherAI	6B	2021+	Open-weight, strong for coding and writing	Yes	2,048
MPT-7B	MosaicML	7B	2023+	Commercial license, fast inference, memory efficient	Yes	65,536
BLOOM	BigScience	176B	2022+	Multilingual, open research project, transparency	Yes	2,048
LLaMA 3.1-70B-Instruct	Meta	70B	2024	Instruction-finetuned, improved over LLaMA 2	Yes	8,000
PaliGemma 2 Mix	Google	Unknown	2025	Multimodal (images + text), aligned with Gemma	Yes	8,192
DolphinGemma	Google	Unknown	2025	Focused on conversational agents, compact & efficient	Yes	8,192
GPT-o4-mini	OpenAI	Unknown	2025	Small, fast version of GPT-4o for API use	No	128,000
GPT-4.1	OpenAI	Unknown	2024	Less latency than GPT-4, used in ChatGPT early 2024	No	1,000,000
Gemini 2.5 Flash	Google	Unknown	2025	Extremely fast and efficient version of Gemini	No	1,000,000
GPT-o3	OpenAI	Unknown	2025	Compact LLM, optimized for API and embedded systems	No	128,000
LLaMA 3	Meta	8B / 70B	2024	Instruction-tuned, top open-source performance	Yes	128,000
TxGemma	Google	Tiny / Small	2025	Minimal deployment footprint, transformer-optimized	Yes	8,192
SIMA	Google DeepMind	Unknown	2024	“Embodied” AI that controls agents in virtual worlds	No	Unknown
Habermas Machine	Google Research	Unknown	2025	Philosophy-aligned reasoning, trained on complex arguments	No	Unknown
GPT-4o	OpenAI	Unknown	2024	Omnimodal (text, vision, audio), super fast, free on ChatGPT	No	128,000
Gemini 2.5	Google	Unknown	2025	Best-in-class multimodal model	No	1,000,000
Claude 3.5	Anthropic	Unknown	2024	Balanced reasoning + helpfulness + speed	No	200,000
Mistral 7B	Mistral	7B	2023	Top open model in its class, efficient & fast	Yes	32,000
Ernie 4.5 / Ernie X1	Baidu	Unknown	2024-2025	Strong in Chinese NLP, multi-modal capabilities	No	128,000
Falcon 180B	TII (UAE)	180B	2023	Open-weight, top performing for its size	Yes	2,048
Granite	IBM	Unknown	2023-2024	Built for enterprise AI, used in WatsonX	No	8,000
LaMDA	Google	Unknown	2022+	Conversational model precursor to Bard/Gemini	No	8,192
Orca	Microsoft Research	13B	2023	Training data distilled from GPT-4, logic-rich	No	4,096
PaLM	Google	540B	2022+	Predecessor to Gemini, large multilingual model	No	8,192
Phi	Microsoft	1.3B / 3.8B / 7B	2023–2024	Small models with high performance on reasoning	Yes	128,000
StableLM	Stability AI	3B / 7B	2023	Creative text generation, open weights	Yes	8,192
Tülu 3	LMSYS / Together AI	70B	2024–2025	Fine-tuned from LLaMA 3.1, focused on helpfulness	Yes	8,000
Vicuna 33B	LMSYS	33B	2023	Fine-tuned from LLaMA, excellent for chat-like use	Yes	8,000

In-Depth Overview of Top LLMs Shaping AI in 2025

1. OpenAI GPT-4.5

OpenAI’s GPT-4.5 builds upon its predecessors with enhanced reasoning capabilities and a significant reduction in hallucination rates. It’s widely used across industries for tasks requiring advanced language understanding.

• Context Length: Supports up to 128K tokens, enabling coherent processing of entire books or lengthy documents.

• Multimodal Abilities: Handles text, images, and voice inputs, enhancing versatility across applications.

• Performance Enhancements:

  • 27% improvement in solving complex math problems compared to GPT-4o.

  • 7–10% boost in coding accuracy.

  • Reduced hallucination rate to 37.1%, down from GPT-4o’s 61.8%.

• Emotional Intelligence: Offers more natural, empathetic interactions, making it suitable for customer service and personal assistants.

• Efficiency: Achieves 10x computational efficiency over GPT-4o, resulting in faster responses and lower energy consumption.

2. Anthropic Claude 3.7 Sonnet

 

Claude 3.7 Sonnet by Anthropic offers robust performance in multi-turn conversations and complex reasoning tasks. Its safety-focused design makes it suitable for sensitive applications.

• Hybrid Reasoning Model: Combines rapid responses with detailed, step-by-step problem-solving.

• Adjustable Token Budget: Allows users to control the depth of reasoning by setting the model’s “thinking” duration.

• Real-World Optimization: Focuses on practical tasks over benchmark performance, aligning better with business applications.

• Integration with Apple: Collaborating with Apple to develop an AI-powered coding assistant within Xcode, streamlining code writing, editing, and testing.

3. Google Gemini 2.5 Pro

Gemini 2.5 Pro integrates text, image, and code processing, making it ideal for multi-modal applications. It’s particularly effective in product development and customer support scenarios.

• Multimodal Capabilities: Enhanced support for text, images, and video inputs.

• Integration with NotebookLM: Powers Google’s NotebookLM with advanced reasoning, particularly for complex, multi-step questions.

• Upcoming iPhone Integration: Google is nearing an agreement with Apple to integrate Gemini into iPhones, enhancing Siri’s capabilities.

• Subscription Tiers: Plans to introduce “Gemini Pro” and “Gemini Ultra” tiers, offering varying levels of features and limitations.

4. Meta LLaMA 4

 

Meta’s LLaMA 4 series, including Scout and Maverick models, demonstrates improved handling of contentious topics and reduced political bias. These models are designed for open-weight deployment, balancing openness with performance.

• Open-Source Focus: Continues Meta’s commitment to open-source models, promoting flexibility and community collaboration.

• Developer Tools: Introduced a new Llama API and partnerships aimed at faster AI deployment.

• Challenges: Despite infrastructure ambitions, Meta faces criticism for lagging behind competitors in releasing advanced reasoning models.

5. xAI Grok-3

 

 

Developed by Elon Musk’s xAI, Grok-3 is tailored for enterprise applications, particularly in the financial sector. Its integration with Palantir and TWG Global underscores its enterprise readiness.

• Big Brain Mode: An advanced setting that amplifies the model’s computational and reasoning abilities, allowing for deeper contextual understanding.

• Real-Time Integration: Deeply integrated with X (formerly Twitter), enabling real-time data analysis and dynamic discussions.

• Multimodal Capabilities: Processes text, images, and code, making it a comprehensive AI assistant.

• Controversies: Faces scrutiny over political bias and safety concerns due to unfiltered responses.

6. DeepSeek R1

DeepSeek R1 from China has gained attention for its strong performance in reasoning tasks and its open-source availability, making it a favorite among developers seeking customizable solutions.

• Reinforcement Learning Training: Trained via large-scale reinforcement learning without supervised fine-tuning, emphasizing reasoning capabilities.

• Open-Source Models: Released under MIT license, including models ranging from 1.5B to 70B parameters.

• Performance: Outperforms models like Gemini 2.0 Pro and OpenAI o1 in bilingual complex reasoning tasks, particularly in medical domains.

7. Alibaba Qwen 3

 

Qwen 3 by Alibaba boasts a massive 235 billion parameters, offering exceptional capabilities in language understanding and generation. Its open-source nature allows for broad customization.

• Multilingual Proficiency: Excels in both Chinese and English, making it suitable for global applications.

• Enterprise Integration: Designed for seamless integration into Alibaba’s ecosystem, supporting various business applications.

• Open-Source Commitment: Continues Alibaba’s tradition of releasing models for community use, fostering innovation.

8. Google Gemma 3

 

Gemma 3 is designed for efficiency, capable of running on a single GPU. Its various sizes cater to different deployment needs, from on-device applications to larger-scale implementations.

• Lightweight Design: Optimized for efficiency, making it suitable for deployment on devices with limited resources.

• On-Device Processing: Supports on-device AI tasks, reducing latency and enhancing privacy.

• Integration with Google Services: Works seamlessly with Google’s suite of applications, enhancing user experience.

9. Cohere Command R+

 

Cohere’s Command R+ excels in retrieval-augmented generation tasks, making it suitable for applications that require up-to-date information and context-aware responses.

• Enterprise Focus: Tailored for business applications, offering robust performance in text-based tasks.

• Long-Context Processing: Handles extensive documents efficiently, making it ideal for legal and research purposes.

• Customization: Allows enterprises to fine-tune the model according to specific needs, enhancing relevance and accuracy.

10. Mistral Large-Instruct-2407

Mistral’s latest model offers strong instruction-following capabilities, making it effective for educational tools and enterprise training applications.

• Instruction-Tuned: Optimized for following detailed instructions, making it suitable for complex task execution.

• Open-Source Availability: Released for community use, encouraging experimentation and development.

• Performance: Demonstrates strong capabilities in structured tasks, though specific benchmarks are less publicized.

11. Flower AI Collective-1

Collective-1 stands out for its decentralized training approach, utilizing distributed computing resources and privacy-sensitive data. This model represents a shift towards more democratized AI development.

• Decentralized Training: Developed by Flower AI and Vana, Collective-1 is a 7-billion-parameter LLM trained across hundreds of internet-connected computers, reducing reliance on centralized data centers.

• Privacy-Centric Data: Utilizes private user data from platforms like X, Reddit, and Telegram, emphasizing responsible data handling.

• Open-Source Tools: Released Photon, an open-source tool to enhance distributed training efficiency.

• Future Plans: Aiming to scale up to 100 billion parameters and incorporate multimodal training with images and audio.

12. NeoBERT

NeoBERT revitalizes the BERT architecture with modern training techniques, achieving state-of-the-art results in various NLP benchmarks while maintaining a compact size.

• Next-Generation Encoder: Introduces a modernized BERT architecture with an optimal depth-to-width ratio and extended context length of 4,096 tokens.

• Compact yet Powerful: Despite having only 250 million parameters, it outperforms larger models like BERT Large and RoBERTa Large on the MTEB benchmark.

• Open Access: All code, data, checkpoints, and training scripts are publicly available to accelerate research and adoption.

13. EleutherAI GPT-J

GPT-J by EleutherAI is an open-source model that continues to be a popular choice for developers seeking a balance between performance and accessibility.

• Open-Source Initiative: GPT-J is a 6-billion-parameter model developed by EleutherAI, aiming to democratize access to powerful language models.

• Versatile Applications: Supports a wide range of NLP tasks, including text generation, summarization, and translation.

• Community-Driven: Continues to be a foundation for various research projects and applications in 2025.

14. MosaicML MPT-7B

MPT-7B offers a versatile platform for developers, with a focus on efficient training and deployment, making it suitable for a range of applications from chatbots to content generation.

• Efficient Architecture: A 6.7-billion-parameter decoder-only transformer utilizing FlashAttention and ALiBi for improved performance.

• Training Data: Trained on a diverse dataset of 1 trillion tokens, including sources like mC4, RedPajama, and The Stack.

• Optimized for Deployment: Designed for efficient training and inference, making it suitable for various applications.

15. BigScience BLOOM

BLOOM is a multilingual model developed through a collaborative effort, supporting a wide array of languages and fostering inclusivity in AI applications.

• Massive Multilingual Model: A 176-billion-parameter open-access language model supporting 46 natural and 13 programming languages.

• Collaborative Effort: Developed by over 1,000 researchers worldwide to promote transparency and inclusivity in AI development.

• Open Access: Available under the Responsible AI License, facilitating research and application across various domains.

16. Meta LLaMA 3.1-70B-Instruct

This instruction-tuned model from Meta offers robust performance in various tasks, benefiting from a large parameter size and extensive training data.

• Instruction-Tuned Model: A 70-billion-parameter version of LLaMA 3.1, fine-tuned for following detailed instructions and complex task execution.

• Open-Source Commitment: Continues Meta’s tradition of releasing models for community use, fostering innovation and research.

• Versatile Applications: Suitable for a wide range of NLP tasks, including question answering, summarization, and code generation.

17. Google PaliGemma 2 Mix

PaliGemma 2 Mix is fine-tuned for multiple tasks, offering flexibility in applications ranging from vision-language tasks to general NLP applications.

• Multimodal Capabilities: Combines text, image, and audio processing, enabling more comprehensive understanding and generation.

• Integration with Google Ecosystem: Designed to work seamlessly with Google’s suite of applications, enhancing user experience.

• Optimized Performance: Improved efficiency and accuracy over its predecessors, making it suitable for various applications.

18. Google DolphinGemma

DolphinGemma is an innovative model aimed at decoding dolphin communication, showcasing the potential of AI in understanding non-human languages.

• AI-Powered Dolphin Communication: Developed in collaboration with Georgia Tech and the Wild Dolphin Project, DolphinGemma analyzes and recreates dolphin sounds to facilitate interspecies communication.

• Advanced Sound Analysis: Utilizes Google’s SoundStream tokenizer to identify patterns correlated with dolphin behavior.

• CHAT System Integration: Works with the Cetacean Hearing Augmentation Telemetry system to enable real-time two-way communication between humans and dolphins.

19. OpenAI GPT-o4-mini

GPT-o4-mini provides a lightweight alternative within the GPT-4 series, suitable for applications where computational resources are limited.

• Cost-Effective Model: Offers a balance between performance and affordability, with input costs at $0.15 per million tokens and output costs at $0.60 per million tokens.

• Multimodal Support: Handles text, image, and audio inputs, enhancing versatility across applications.

• Improved Safety: Incorporates measures to resist prompt injection and other adversarial attacks, increasing reliability.

20. OpenAI GPT-4.1

GPT-4.1 continues to offer strong performance across various NLP tasks, serving as a reliable model for developers and enterprises alike.

• Extended Context: Supports up to 1 million tokens, significantly surpassing previous models like GPT-4o’s 128K token limit.

• Enhanced Capabilities: Improved performance in coding, instruction-following, and long-context understanding.

• Multiple Versions: Available in standard, Mini, and Nano versions, catering to various performance and cost requirements.

21. Google Gemini 2.5 Flash

Gemini 2.5 Flash powers Google’s NotebookLM, enhancing users’ experience by providing more comprehensive answers, particularly for complex and multi-step reasoning questions.

• Optimized for Speed: A lightweight variant of Gemini 2.5 Pro, designed for rapid, cost-effective inference.

• NotebookLM Integration: Powers Google’s NotebookLM, enhancing its ability to handle complex, multi-step reasoning tasks.

• Multilingual Support: Features like Audio Overviews support over 50 languages, catering to a diverse user base.

• Standalone App: NotebookLM is transitioning into a standalone app, with pre-registration available on major app stores.

22. OpenAI GPT-o3

GPT-o3 is part of OpenAI’s ongoing efforts to refine and expand its language model offerings, providing developers with more options for various applications.

• Compact Efficiency: A smaller, more efficient model in OpenAI’s lineup, balancing performance with resource usage.

• Versatility: Suitable for a range of applications, from chatbots to content generation, where computational resources are limited.

23. Meta LLaMA 3

LLaMA 3 is a collection of pretrained and instruction-tuned generative text models available in 8 billion (8B) and 70 billion (70B) parameter sizes, optimized for dialogue use cases.

• Enhanced Performance: Offers significant improvements in language understanding and mathematical problem-solving.

• Scalability: Available in various sizes, including a massive 405-billion-parameter model, catering to different application needs.

• Open Access: Continues Meta’s commitment to open-source AI, fostering community-driven innovation.

24. Google TxGemma

TxGemma is an open-source model designed to improve the efficiency of therapeutics development, highlighting AI’s potential in the healthcare sector.

• Therapeutic Focus: Specialized in biomedical and therapeutic applications, aiding in drug discovery and development.

• Model Variants: Available in 2B, 9B, and 27B parameter sizes, fine-tuned on diverse biomedical datasets.

• Agentic-Tx: An advanced system built upon TxGemma, capable of managing complex workflows and reasoning tasks in therapeutic research.

25. Google SIMA

SIMA is an AI agent capable of understanding and following natural language instructions to complete tasks across various 3D virtual environments, demonstrating adaptability to new tasks and settings.

• Simulation Intelligence: Designed to operate within simulated environments, enabling advanced reasoning and decision-making.

• Applications: Useful in training autonomous systems and conducting complex simulations for research and development.

26. Google Habermas Machine

The Habermas Machine is an experimental AI model trained to help identify and present areas of overlap among group members, aiming to facilitate consensus in discussions.

• AI Mediation Tool: Developed to facilitate consensus in contentious discussions by generating balanced group statements.

• Consensus Building: Demonstrated effectiveness in reducing polarization in large-scale studies.

• Limitations: While effective in fostering agreement, it may not fully capture the depth of minority perspectives.

27. OpenAI GPT-4o

GPT-4o is designed to feel more human-like during interactions and boasts improvements in unsupervised learning, enhancing its ability to recognize patterns and generate creative insights.

• Multimodal Mastery: Integrates text, voice, and vision, enabling seamless interaction across different data types.

• Real-Time Interaction: Capable of engaging in natural, real-time conversations with emotional nuance and even singing capabilities.

• Accessibility: Offers advanced features to free users, democratizing access to cutting-edge AI technology.

28. Google Gemini 2.5

Gemini 2.5 is part of Google’s Gemini series, integrating text, image, and code processing, making it ideal for multi-modal applications.

• Advanced Reasoning: Excels in complex tasks, outperforming competitors in various benchmarks.

• Multimodal Capabilities: Handles text, audio, images, video, and code, making it versatile for numerous applications.

• Extended Context: Supports up to 2 million tokens, allowing for in-depth analysis and understanding.

29. Anthropic Claude 3.5

Claude 3.5 offers robust performance in multi-turn conversations and complex reasoning tasks, with a focus on safety and reliability.

• Enhanced Performance: Surpasses previous models in reasoning, coding, and visual tasks.

• Cost-Effective: Offers high performance at a fraction of the cost, making it accessible for various use cases.

• Collaborative Features: Introduces “Artefacts,” enabling users to generate and edit content in a shared workspace

30. Mistral 7B

Mistral 7B is a versatile model optimized for efficient training and deployment, suitable for a range of applications from chatbots to content generation.

• Efficient Design: A 7-billion-parameter model optimized for performance and speed.

• Instruction-Tuned: Fine-tuned to follow instructions accurately, making it suitable for various applications.

• Open Source: Released under the Apache 2.0 license, promoting transparency and community involvement.

31. Ernie 4.5 / Ernie X1 (Baidu)

Baidu’s Ernie 4.5 introduces native multimodal capabilities, enabling it to process and convert between text, video, images, and audio. Ernie X1, a reasoning model, offers advanced understanding, planning, and reflection abilities, positioning it as a cost-effective competitor to models like DeepSeek R1.

• Ernie 4.5: An upgraded version with enhanced language abilities, improved logic and memory, and high emotional intelligence to understand memes and satire.

• Ernie X1: Designed to rival DeepSeek’s R1, it offers superior capabilities in understanding, planning, reflection, and evolution at half the cost.

• Open-Source Commitment: Baidu plans to make Ernie models open-source, aligning with China’s pro-tech industry policies.

32. Falcon 180B (Technology Innovation Institute, UAE)

Developed by Abu Dhabi’s Technology Innovation Institute, Falcon 180B is a 180-billion-parameter model trained on over 3.5 trillion tokens. It achieves performance comparable to leading models like PaLM-2-Large, emphasizing efficiency and open-source accessibility.

• Scale & Performance: With 180 billion parameters trained on 3.5 trillion tokens, Falcon 180B ranks #1 on Hugging Face’s leaderboard for open-access LLMs.

• Open Access: Available for both research and commercial use under the Falcon 180B TII License.

• Multilingual Support: Supports multiple languages, including English, German, Spanish, and French.

33. Granite (IBM)

IBM’s Granite series comprises decoder-only AI foundation models designed for enterprise applications. Integrated into IBM’s Watsonx platform, Granite models are trained on diverse datasets, including legal and financial documents, and some code models are open-sourced under the Apache 2.0 license.

• Enterprise Focus: A series of decoder-only AI foundation models optimized for business applications, including code generation and legal document analysis.

• Open-Source Components: Some code models are open-sourced under Apache 2.0, promoting transparency and collaboration.

• Integration: Designed to work seamlessly with IBM’s Watsonx platform.

34. LaMDA (Google)

LaMDA (Language Model for Dialogue Applications) is a conversational AI model developed by Google. While it has been succeeded by models like PaLM and Gemini, LaMDA laid the groundwork for Google’s advancements in dialogue-based AI applications.

• Conversational AI: Specialized in dialogue applications, LaMDA was trained on 1.56 trillion words to engage in open-ended conversations.

• Evolution: Replaced by PaLM and later by the Gemini family, reflecting Google’s progression in LLM development.

• Notable Incident: Gained attention when a Google engineer claimed it had become sentient, sparking debates on AI consciousness.

35. Orca (Microsoft Research)

Orca is a model from Microsoft Research that focuses on mimicking the reasoning processes of larger models through imitation learning. It aims to achieve high performance with fewer parameters by learning from the outputs of more extensive models.

• Efficient Training: Utilizes a technique called “explanation tuning” to learn from the reasoning processes of larger models, achieving high performance with fewer parameters.

• Accessibility: Designed to be more accessible and efficient, making it suitable for a wider range of applications.

36. PaLM (Google)

PaLM (Pathways Language Model) is a 540-billion-parameter model developed by Google AI. It excels in a wide range of tasks, including commonsense reasoning and code generation, and has been adapted into specialized versions like Med-PaLM for medical applications.

• Scale: A 540-billion-parameter model capable of a wide range of tasks, including commonsense reasoning, arithmetic, and code generation.

• Variants: Includes Med-PaLM for medical applications and PaLM-E for vision-language tasks.

• Successor: Replaced by the Gemini family, which continues to build on its capabilities.

37. Phi (Microsoft)

Phi is a lightweight language model developed by Microsoft, designed to achieve competitive performance with a smaller parameter count. It focuses on efficiency and accessibility for various applications.

• Compact Models: Phi-3 Mini, Small, and Medium are designed to run efficiently on devices like smartphones while maintaining competitive performance.

• Training: Uses an updated, scaled-up training dataset to enhance capabilities despite smaller model sizes.

• Deployment: Suitable for local use on devices, promoting privacy and reducing reliance on cloud services.

38. StableLM (Stability AI)

StableLM is an open-source language model series by Stability AI, emphasizing transparency and accessibility. It’s designed to support a wide range of applications, from research to deployment in production environments.

• Open-Source Initiative: A series of open-source LLMs aimed at promoting transparency and community-driven development.

• Versatility: Designed for a wide range of applications, from research to commercial use.

• Community Engagement: Encourages contributions from developers and researchers to enhance model capabilities.

39. Tülu 3

Tülu 3 is a fine-tuned large language model that builds upon previous iterations to enhance performance in instruction-following tasks. It aims to provide more accurate and context-aware responses in various applications.

• Instruction Tuning: An open-source model fine-tuned for following instructions accurately, making it suitable for various NLP tasks.

• Community Collaboration: Developed with input from the AI research community to ensure broad applicability and effectiveness.

40. Vicuna 33B

Vicuna 33B is an open-source chatbot model fine-tuned from LLaMA, designed to deliver high-quality conversational abilities. It serves as a cost-effective alternative to proprietary models, supporting research and development in conversational AI.

• Chatbot Specialization: A fine-tuned version of LLaMA 2, optimized for conversational applications.

• Open Access: Available for research and commercial use, promoting wider adoption and experimentation.

• Performance: Demonstrates strong capabilities in generating coherent and contextually relevant responses.

Conclusion

The landscape of large language models in 2025 is marked by diversity and specialization. From models tailored for enterprise applications to those pushing the boundaries of multilingual understanding, the options available cater to a wide range of needs. As AI continues to integrate into various aspects of society, these LLMs will play a pivotal role in shaping the future of human-computer interaction.