Top 7 Open-Source LLMs in 2025

Open-source large Llanguage models (LLMs) are getting better with time, and they offer more cost-effective alternatives compared to proprietary models. You can fine-tune them, use them locally, or even serve them on your own server or cloud with enhanced privacy and security. This means you have full control when using these open-source models. So, which model should you use for your project? In this article, we will explore the top 7 LLMs based on their overall scores across multiple benchmarks. These models outperform 95% of proprietary options in terms of code generation, reasoning, question answering, and solving complex text tasks.

1. DeepSeek R1

DeepSeek R1, an open-source reasoning model developed by the DeepSeek AI, is designed to excel in tasks requiring logical inference, mathematical problem-solving, and real-time decision-making. Unlike traditional language models, reasoning models like DeepSeek R1 stand out due to their ability to transparently demonstrate how they arrive at conclusions, offering step-by-step explanations of their thought processes.

Key Features:

• Superior reasoning capabilities: Excels at complex problem-solving and logical reasoning.
• Efficient architecture: The MoE framework activates only a subset of the model’s parameters for each query, optimizing performance.
• Cross-domain problem-solving: Designed for versatility across various applications with minimal fine-tuning.
• Multilingual support: Proficient in over 20 languages.
• Context window: Impressive 128K token context window.
• Specialized knowledge: Strong performance in scientific and technical domains.

DeepSeek R1 shines in research applications, technical documentation, and complex reasoning tasks. Its ability to process extensive context makes it ideal for document analysis and summarization.

2. Qwen2.5-72B-Instruct

Developed by Alibaba’s DAMO Academy, Qwen2.5-72B is a powerful instruction-tuned large language model with 72 billion parameters, excelling in coding, mathematics, multilingual tasks (29+ languages), long-context understanding (up to 128K tokens), and generating structured outputs like JSON

Key Features:

• Massive scale: 72.7 billion parameters, with 70 billion non-embedding parameters.
• Advanced architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
• Multilingual support: Proficient in 29 languages.
• Strong mathematical abilities: Excels at calculation and mathematical reasoning.
• Structured output generation: Optimized for generating JSON and other structured data formats.

This model is particularly effective for enterprise applications, content creation, and educational tools. Its mathematical prowess makes it suitable for data analysis and technical problem-solving.

3. Llama 3.3

Llama 3.3-70B is Meta’s multilingual, instruction-tuned large language model optimized for dialogue, supporting 8+ languages, long-context understanding (128K tokens), and excelling in benchmarks against open and closed models.

Key Features:

• Balanced performance: Strong across general knowledge, reasoning, and coding.
• Efficient resource usage: Optimized for better performance on consumer hardware.
• Extensive context window: Supports up to 128K tokens
• Multilingual capabilities: Supports English, French, German, Hindi, Italian, Portuguese, Spanish, and Thai.
• Well-documented: Extensive documentation and community support.

Llama 3.3 serves as an excellent general-purpose model for applications ranging from chatbots to content generation.

4. Mistral-Large-Instruct-2407

Mistral-Large-Instruct-2407 is a 123B-parameter multilingual large language model excelling in reasoning, coding (80+ languages), agentic capabilities (native function calling, JSON output), and long-context understanding (128K tokens).

Key Features:

• Exceptional language understanding: Superior natural language processing capabilities.
• Advanced architecture: Dense large language model with 123 billion parameters.
• Extensive context window: Supports up to 131K tokens.
• State-of-the-art performance: Excels in reasoning, knowledge, and coding tasks.
• Low hallucination rate: Higher factual accuracy than many competitors.

Mistral-Large-Instruct-2407 is particularly valuable for content creation, customer service applications, and scenarios requiring high factual accuracy. Its creative abilities make it suitable for marketing and entertainment applications.

5. Llama-3.1-70B-Instruct

An earlier release in Meta’s Llama 3 series, the 70B model remains highly competitive with newer releases. Its instruction-tuned version delivers excellent performance across diverse tasks.

Key Features:

• Robust reasoning: Strong logical and analytical capabilities.
• Extensive knowledge base: Comprehensive general knowledge.
• Multilingual support: Proficient in multiple languages.
• Community support: Large ecosystem of tools and fine-tuned variants.

This model excels in research applications, complex reasoning tasks, and enterprise solutions. Its established ecosystem makes implementation straightforward for developers.

6. Phi-4

Microsoft’s Phi-4 demonstrates that smaller models can deliver exceptional performance when architecture and training are optimized. Despite its relatively modest parameter count, it competes with much larger models.

Key Features:

• Efficiency: Outstanding performance-to-size ratio.
• Code generation: Particularly strong at programming tasks.
• Strong reasoning capabilities: Excels in tasks requiring advanced reasoning.
• Resource requirements: Can run on consumer hardware with minimal resources.

Phi-4 is ideal for applications with resource constraints, edge computing, and mobile applications. Its efficiency makes it suitable for deployment in environments where larger models would be impractical.

7. Gemma-2-9b-it

Gemma-2-9b-it is a lightweight, state-of-the-art open text-to-text model from Google, built on Gemini research, optimized for reasoning, summarization, and question answering, with open weights and deployable on resource-limited devices.

Key Features:

• Compact yet powerful: 9 billion parameter model with competitive performance.
• Lightweight deployment: Minimal resource requirements
• Efficient quantization: FP8 quantized version reduces disk size and GPU memory requirements by approximately 50%.
• Hybrid attention mechanism: Combines sliding window attention for local context and full quadratic global attention for long-range dependencies.
• Instruction following: Precise adherence to complex instructions.

Gemma-2-9b-it is particularly valuable for applications requiring deployment on resource-constrained devices. Its balanced capabilities make it suitable for chatbots, content moderation, and educational tools.

Conclusion

The open-source LLM landscape in 2025 offers impressive options that rival proprietary alternatives. These seven models provide varying strengths and capabilities to suit different application requirements and resource constraints. The rapid advancement of open-source models continues to democratize access to cutting-edge AI technology, enabling developers and organizations to build sophisticated applications without depending on proprietary solutions.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.