In the last few years, we have seen businesses lean towards the adoption of AI solutions. Do you know that AI text generation tools use powerful large language models (LLMs)?
Over the past few years, hundreds of LLMs have emerged on the scene. Some are open-source, while others are proprietary large language models. Smaller organizations usually prefer open-source LLMs because they are free to use and modifiable to match their needs.
Today, we will be exploring the top 7 open-source LLMs among the hundreds available that you must use in 2025.
Table of Contents
- Introduction
- 1. Llama 3
- 2. Mixtral 8x7B
- 3. Phi-3
- 4. SOLAR
- 5. Gemma
- 6. OpenHermes
- 7. Orca 2
- 8. DeepSeek LLM
- 9. Yi
- 10. StableLM
- Comparison Matrix
- Hardware Requirements
- Conclusion
Introduction
Open-source Large Language Models (LLMs) have revolutionized AI accessibility, enabling organizations to deploy powerful language models without vendor lock-in or usage restrictions. This guide explores the top open-source LLMs of 2025, comparing their capabilities, requirements, and optimal use cases.
1. Llama 3
We have seen multiple top names like OpenAI introduce proprietary LLMs. However, a big name, Meta, didn’t shy away from releasing one of the best open-source LLM, Llama 3.1. It was released on July 23, 2024.
Llama 3.1 offers unmatched capabilities while staying openly available to developers. It is known for its scalability and high performance. It supports eight languages and supports a whopping 128,000 tokens context length. That means it is able to understand long texts and engage in more complex conversations. It is a powerhouse for creating synthetic data and improving smaller models.
Developer: Meta AI
License: Llama Community License
Latest Version: 3.0
Model Sizes: 7B, 13B, 34B, 70B
Key Features:
- Enhanced multilingual capabilities
- Improved context window (128K tokens)
- Advanced reasoning abilities
- Robust instruction following
- Enhanced code generation
Performance Metrics:
- MMLU Score: 86.4% (70B model)
- GSM8K: 82.3%
- HumanEval: 78.5%
- BBH: 75.2%
Best Used For:
- Enterprise applications
- Research projects
- Multi-language deployment
- Complex reasoning tasks
- Code generation
2. Mixtral 8x7B
Developer: Mistral AI
License: Apache 2.0
Latest Version: 2.0
Architecture: Mixture of Experts
Key Features:
- Sparse Mixture of Experts (MoE)
- Multiple specialized experts
- Efficient compute utilization
- Strong multilingual support
- Advanced context processing
Performance Metrics:
- MMLU Score: 83.2%
- GSM8K: 80.1%
- HumanEval: 75.8%
- BBH: 73.5%
Best Used For:
- Resource-efficient deployment
- Multilingual applications
- Complex analysis tasks
- Research applications
3. Phi-3
Developer: Microsoft Research
License: MIT
Latest Version: 3.0
Model Sizes: 3B, 7B, 14B
Key Features:
- Small model size
- Strong reasoning capabilities
- Efficient training approach
- Low resource requirements
- Strong code generation
Performance Metrics:
- MMLU Score: 78.5% (14B model)
- GSM8K: 75.2%
- HumanEval: 72.3%
- BBH: 70.1%
Best Used For:
- Edge deployment
- Educational applications
- Code assistance
- Research projects
4. SOLAR
Developer: Upstage AI
License: Apache 2.0
Latest Version: 2.0
Model Sizes: 10.7B, 70B
Key Features:
- Extended context window
- Strong mathematical reasoning
- Advanced task completion
- Robust instruction following
- Research-focused design
Performance Metrics:
- MMLU Score: 81.3% (70B model)
- GSM8K: 78.9%
- HumanEval: 73.2%
- BBH: 72.8%
Best Used For:
- Academic research
- Complex problem solving
- Mathematical applications
- Scientific computing
5. Gemma
Developer: Google
License: Gemma Community License
Latest Version: 1.0
Model Sizes: 2B, 7B
Key Features:
- Efficient architecture
- Strong safety features
- Research transparency
- Comprehensive documentation
- Robust evaluation metrics
Performance Metrics:
- MMLU Score: 75.2% (7B model)
- GSM8K: 72.1%
- HumanEval: 68.5%
- BBH: 67.9%
Best Used For:
- Educational purposes
- Research projects
- Safe deployment
- Resource-constrained environments
6. OpenHermes
Developer: Community
License: Apache 2.0
Latest Version: 3.0
Model Size: 7B, 13B
Key Features:
- Community-driven development
- Strong instruction following
- Efficient fine-tuning
- Regular updates
- Active community support
Performance Metrics:
- MMLU Score: 76.8% (13B model)
- GSM8K: 73.5%
- HumanEval: 70.2%
- BBH: 69.8%
Best Used For:
- Community projects
- Educational purposes
- Research applications
- Collaborative development
7. Orca 2
Developer: Microsoft Research
Latest Version: 2.0
License: MIT
Model Sizes: 7B, 13B, 70B
Key Features:
- Advanced reasoning capabilities
- Strong task completion
- Efficient training methodology
- Research-focused design
- Comprehensive documentation
Performance Metrics:
- MMLU Score: 80.5% (70B model)
- GSM8K: 77.8%
- HumanEval: 72.9%
- BBH: 71.5%
Best Used For:
- Research applications
- Complex reasoning tasks
- Educational purposes
- Academic projects
8. DeepSeek LLM
Developer: DeepSeek
License: Apache 2.0
Latest Version: 2.0
Model Sizes: 7B, 33B, 67B
Key Features:
- Strong code generation
- Advanced reasoning
- Multilingual support
- Regular updates
- Community focus
Performance Metrics:
- MMLU Score: 79.8% (67B model)
- GSM8K: 76.5%
- HumanEval: 74.8%
- BBH: 70.9%
Best Used For:
- Code development
- Research projects
- Multilingual applications
- Enterprise deployment
9. Yi
Developer: 01.AI
License: Apache 2.0
Latest Version: 2.0
Model Sizes: 6B, 34B, 101B
Key Features:
- Large parameter count
- Strong multilingual support
- Advanced reasoning
- Regular updates
- Community engagement
Performance Metrics:
- MMLU Score: 82.1% (101B model)
- GSM8K: 78.9%
- HumanEval: 73.5%
- BBH: 72.8%
Best Used For:
- Large-scale deployment
- Research applications
- Multilingual projects
- Enterprise solutions
10. StableLM
Developer: Stability AI
License: Apache 2.0
Latest Version: 2.0
Model Sizes: 3B, 7B, 15B
Key Features:
- Stable performance
- Efficient architecture
- Regular updates
- Strong community
- Easy deployment
Performance Metrics:
- MMLU Score: 74.5% (15B model)
- GSM8K: 70.2%
- HumanEval: 67.8%
- BBH: 66.5%
Best Used For:
- Stable deployment
- Research projects
- Educational purposes
- Community development
Comparison Matrix
| Model | Size Range | License | Context Window | MMLU Score* | Best For |
|---|---|---|---|---|---|
| Llama 3 | 7B-70B | Community | 128K | 86.4% | Enterprise |
| Mixtral 8x7B | 8x7B | Apache 2.0 | 32K | 83.2% | Efficiency |
| Phi-3 | 3B-14B | MIT | 16K | 78.5% | Edge |
| SOLAR | 10.7B-70B | Apache 2.0 | 64K | 81.3% | Research |
| Gemma | 2B-7B | Community | 8K | 75.2% | Education |
| OpenHermes | 7B-13B | Apache 2.0 | 16K | 76.8% | Community |
| Orca 2 | 7B-70B | MIT | 32K | 80.5% | Research |
| DeepSeek | 7B-67B | Apache 2.0 | 32K | 79.8% | Code |
| Yi | 6B-101B | Apache 2.0 | 32K | 82.1% | Enterprise |
| StableLM | 3B-15B | Apache 2.0 | 16K | 74.5% | Stability |
*MMLU scores reported for largest model size
Hardware Requirements
Minimum Requirements (7B models):
- GPU: 8GB VRAM
- RAM: 16GB
- Storage: 20GB
Recommended Requirements (70B+ models):
- GPU: 48GB+ VRAM
- RAM: 64GB+
- Storage: 100GB+ NVMe SSD
Conclusion
The open-source LLM landscape in 2025 offers diverse options for various use cases:
- Enterprise Deployment: Llama 3, Yi
- Research: SOLAR, Orca 2
- Edge Computing: Phi-3, Gemma
- Efficiency: Mixtral 8x7B
- Community Development: OpenHermes, StableLM
When selecting an open-source LLM, consider:
- Hardware requirements
- License restrictions
- Performance needs
- Deployment environment
- Community support
- Update frequency
The field continues to evolve rapidly, with new models and improvements regularly emerging. Regular evaluation of new releases and updates is recommended for optimal deployment.