In the evolving AI landscape of 2025, open-source AI models are taking center stage. They offer transparency, cost savings, and customization that proprietary models can’t match. From trillion-parameter research projects to community-built chatbots, these top models represent the cutting edge of open AI. Below are ten influential open-source LLMs developers and researchers should know, along with their key details and use cases.
Meta LLaMA 4 (2025, Meta AI)
The latest in Meta AI’s LLaMA family, LLaMA 4 brings ultra-large-scale language understanding to the open-source world. Released in 2025, LLaMA 4 includes models with up to two trillion parameters, dramatically increasing its reasoning and generation capabilities. It supports tasks ranging from multilingual chat to code generation (via Code LLaMA). Instruction-tuned variants (chat models) are available alongside the base models.
- Release Year & Org: 2025, Meta AI
- Parameter Count: Up to ~2 trillion (largest LLaMA model).
- Capabilities: General language generation, understanding, and translation. Used for chatbots, content creation, and code assistance.
- Unique Features: Chinchilla-optimal training (massive dataset ~15 trillion tokens). Instruction-tuned versions for chat. Openly released weights and code for research and deployment.
- Use Cases: Research and development of custom AI assistants, chatbots, and coding tools. Often fine-tuned for specialized applications in multiple languages.
Moonshot AI Kimi K2 (2025, Moonshot AI)
Kimi K2 is a groundbreaking trillion-parameter Mixture-of-Experts (MoE) model introduced in 2025 by Moonshot AI. With a 1T total parameter scale (32B active per pass), K2 specializes in code generation, math reasoning, and complex agentic workflows. It supports an extremely long context window (up to 128K tokens), making it adept at handling large documents or codebases. The base model can be fine-tuned further, while an instruction-tuned variant (K2-Instruct) is optimized for chat and tool-driven tasks.
- Release Year & Org: 2025, Moonshot AI
- Parameter Count: 1 trillion total (32B active) via an MoE architecture.
- Capabilities: High-performance code generation and debugging, advanced math and logic reasoning, and autonomous agent workflows (multi-step API/tool execution).
- Unique Features: Sparse MoE design for massive scale. MuonClip optimizer for stability. Long-context support (128K tokens). Available in both base and instruction-tuned forms.
- Use Cases: Advanced development assistants (auto-complete, refactoring, pull request reviews), autonomous DevOps agents (cloud orchestration, automated builds), and processing large technical documents. See OrionAI’s Guide to fine-tuning Kimi K2 for adapting it to your tasks.
United AI DeepSeek R1 (2025, United AI)
DeepSeek R1 is a large language model announced by United AI (DeepSeek) in 2025. Its open-release versions come in 70B and 32B parameters, distilled from a larger original model. DeepSeek’s development emphasized chain-of-thought reasoning and math, with benchmark performance on par with top closed models like GPT-4. The code and weights are fully open-source under an MIT license, promoting community use and commercial deployment.
- Release Year & Org: 2025, United AI (DeepSeek)
- Parameter Count: 70B and 32B distilled models (original base was larger).
- Capabilities: Strong reasoning, math problem solving, and code generation. Benchmarks show near-GPT-4 level on various reasoning and coding tests.
- Unique Features: Trained with large-scale post-training reinforcement learning. Fully open-sourced (MIT license). Multi-round reasoning features like context caching.
- Use Cases: Cutting-edge open model for research and development. Good for code assistants, advanced reasoning tasks, and as a GPT-4 alternative where data privacy and customization are needed.
Falcon-180B (2023, Technology Innovation Institute)
Developed by TII in 2023, Falcon-180B is one of the largest freely available LLMs. It has 180 billion parameters and was trained on a multi-trillion-token dataset with diverse multilingual content. Falcon-180B achieves strong accuracy on standard benchmarks and is released under an Apache 2.0 license. It is known for its large (65K token) context window and support for efficient 4-bit and 8-bit quantization.
- Release Year & Org: 2023, Technology Innovation Institute (UAE)
- Parameter Count: 180 billion.
- Capabilities: General-purpose language understanding and generation. Performs well on creative writing, summarization, and can assist with coding tasks.
- Unique Features: Trained on multilingual web data (including Spanish/Arabic). Very large context (up to 65K tokens). Open weights for local deployment and fine-tuning.
- Use Cases: Building chatbots, content generators, and coding tools. Its open license and strong performance make it popular for research, especially in Europe and Asia.
Mistral Mixtral-8x22B (2023, Mistral AI)
The Mixtral-8x22B model from Mistral AI (late 2023) uses a sparse Mixture-of-Experts design to deliver high performance efficiently. It has 39 billion total parameters (eight experts of ~4.8B each) but activates only a fraction at a time. This yields top-tier benchmark scores with lower compute cost. Mistral’s earlier dense 7B model was already notable for outperforming similarly sized alternatives.
- Release Year & Org: 2023, Mistral AI
- Parameter Count: 39 billion total (8 experts × ~4.8B, with 2 experts active). Also offers a 7.3B dense model.
- Capabilities: Strong natural language understanding and generation. Outperforms many models of similar size (e.g. beats LLaMA 13B on zero-shot tests).
- Unique Features: Sparse MoE for high performance-to-compute ratio. Training efficiency and speed. Released under CC BY 4.0 license (fully open).
- Use Cases: Fast and cost-effective AI services. Great for cloud deployment and edge devices where compute is limited. Also a strong base for fine-tuned chatbots or domain-specific applications.
BLOOM (2022, BigScience)
BLOOM is a 176B-parameter multilingual model released by the BigScience research effort in 2022. It was trained on a diverse corpus covering 46 natural languages (including low-resource languages) and 13 programming languages. As the first open project of its scale, BLOOM demonstrated collaborative research on a global stage. The model’s weights are fully open under a responsible use license.
- Release Year & Org: 2022, BigScience (Hugging Face-led consortium)
- Parameter Count: 176 billion.
- Capabilities: Multilingual text generation and translation. Can handle programming languages as well (code generation). Known for handling diverse languages.
- Unique Features: Trained on inclusive, multilingual data (the ROOTS corpus). Emphasizes open science – full weights and training code available under RAIL license.
- Use Cases: Cross-lingual NLP research, translation tools, content creation in various languages, and as a benchmark for large-scale multilingual understanding.
StarCoder (2023, BigCode)
StarCoder is a 15B-parameter model released by BigCode in 2023, trained specifically on public code. It was trained on over 80 GB of source code and supports dozens of programming languages. StarCoder excels at code completion, generation, and explanation, while still retaining general language capabilities. The model is available under an Apache 2.0 license on Hugging Face.
- Release Year & Org: 2023, BigCode (open-source community)
- Parameter Count: 15 billion.
- Capabilities: Code generation and completion (in Python, JavaScript, Java, etc.), code explanation, and summary. Also performs general text tasks moderately well.
- Unique Features: Trained exclusively on code repositories, making it highly adept at programming tasks. Openly released weights and code for local use and fine-tuning.
- Use Cases: Developer tools (IDE auto-complete, copilot-like assistants), automated code review and testing, and educational tools for learning programming.
Code LLaMA (2023, Meta AI)
Code LLaMA is a family of models released by Meta in 2023, fine-tuned on billions of lines of code. It comes in 7B, 34B, and 70B parameter versions. These models are adept at generating, translating, and explaining code in many languages (Python, Java, C++, etc.), as well as handling natural language queries about code. Code LLaMA combines the LLaMA backbone with specialized code training.
- Release Year & Org: 2023, Meta AI
- Parameter Count: 7B, 34B, and 70B variants.
- Capabilities: High-quality code completion and generation, code translation between languages, and context-aware code explanations.
- Unique Features: Instruction-fine-tuned on code. Maintains LLaMA’s efficiency while boosting code understanding. Fully open with weights and inference code.
- Use Cases: Coding assistants, automated refactoring tools, and any application that needs code synthesis or understanding. Ideal for integrating into developer workflows.
Vicuna (2023, LMSYS)
Vicuna is a family of open chatbot models fine-tuned from LLaMA, launched in 2023 by LMSYS (University of Washington). For instance, Vicuna-13B builds on LLaMA by training on user-shared conversations. Despite its smaller size (7B and 13B), Vicuna performs on par with or better than larger closed models in interactive chat tests. It is fully open-source with a permissive license, making it easy to deploy.
- Release Year & Org: 2023, LMSYS (UW and community)
- Parameter Count: 7B and 13B models based on LLaMA.
- Capabilities: Conversational AI and Q&A. Generates detailed, on-topic responses and can follow multi-turn dialogues effectively.
- Unique Features: Trained on high-quality user-generated chat logs. Focused on safety and helpfulness. Completely open weights under CreativeML license.
- Use Cases: Personal assistants, customer support bots, and any chat-based application. Because of its efficiency, it can run on consumer-grade GPUs.
GPT-NeoX-20B (2022, EleutherAI)
GPT-NeoX-20B is a 20-billion-parameter model released by EleutherAI in 2022. It follows the GPT-3 architecture and was trained on the Pile dataset. As one of the larger open models, it serves as a strong performer on many language tasks despite its smaller size relative to newer giants. GPT-NeoX’s fully open release (code and weights) has made it a foundation for other projects like Dolly and RedPajama.
- Release Year & Org: 2022, EleutherAI
- Parameter Count: 20 billion.
- Capabilities: General text generation, summarization, translation, and few-shot learning. Good performance on typical benchmarks for its scale.
- Unique Features: Fully open-source (weights and training code available). Designed to replicate GPT-3-like performance. Basis for community fine-tuning projects.
- Use Cases: Academic research, fine-tuning experiments, and small-scale production uses where newer models aren’t accessible. Commonly used as a starting point for derivative models.