What are Open Source Large Language Models?
Open source large language models (LLMs) are advanced AI systems trained on vast amounts of text data to understand and generate human-like language. These models use transformer architectures and deep learning to process natural language prompts and produce coherent, contextually relevant responses. Open source LLMs democratize access to powerful AI capabilities, enabling developers, researchers, and businesses to deploy, customize, and innovate without proprietary restrictions. They support a wide range of applications from coding assistance and reasoning tasks to multilingual communication and creative content generation.
DeepSeek-R1
DeepSeek-R1 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness.
DeepSeek-R1: Advanced Reasoning Powerhouse
DeepSeek-R1 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. With 671B total parameters in a MoE architecture and 164K context length, it achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. Through carefully designed training methods incorporating cold-start data optimization, it has enhanced overall effectiveness in complex reasoning scenarios.
Pros
- Performance comparable to OpenAI-o1 in reasoning tasks.
- Advanced reinforcement learning optimization.
- Massive 671B parameter MoE architecture.
Cons
- High computational requirements for deployment.
- Higher inference costs due to large parameter count.
Why We Love It
- It delivers state-of-the-art reasoning performance comparable to leading closed-source models while remaining open source and accessible to researchers and developers.
Qwen3-235B-A22B
Qwen3-235B-A22B is the latest large language model in the Qwen series, featuring a Mixture-of-Experts (MoE) architecture with 235B total parameters and 22B activated parameters. This model uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue).
Qwen3-235B-A22B: Versatile Reasoning Excellence
Qwen3-235B-A22B features a sophisticated MoE architecture with 235B total parameters and 22B activated parameters. It uniquely supports seamless switching between thinking mode for complex logical reasoning, math, and coding, and non-thinking mode for efficient dialogue. The model demonstrates superior human preference alignment in creative writing and role-playing, with excellent agent capabilities for tool integration and support for over 100 languages.
Pros
- Dual-mode operation for flexibility and efficiency.
- Superior multilingual support (100+ languages).
- Excellent agent capabilities for tool integration.
Cons
- Complex architecture requires careful deployment planning.
- Higher resource requirements than smaller models.
Why We Love It
- It offers unparalleled flexibility with dual-mode operation, combining efficient dialogue capabilities with advanced reasoning, making it ideal for diverse AI applications.
moonshotai/Kimi-Dev-72B
Kimi-Dev-72B is a new open-source coding large language model achieving 60.4% on SWE-bench Verified, setting a state-of-the-art result among open-source models. Optimized through large-scale reinforcement learning, it autonomously patches real codebases in Docker and earns rewards only when full test suites pass.
Kimi-Dev-72B: State-of-the-Art Coding Excellence
Kimi-Dev-72B is a specialized 72B parameter coding model that achieves 60.4% on SWE-bench Verified, setting a state-of-the-art result among open-source models. Optimized through large-scale reinforcement learning, it autonomously patches real codebases in Docker environments and earns rewards only when full test suites pass. This ensures the model delivers correct, robust, and practical solutions aligned with real-world software engineering standards.
Pros
- State-of-the-art 60.4% performance on SWE-bench Verified.
- Real-world codebase patching capabilities.
- Reinforcement learning optimization for practical solutions.
Cons
- Specialized primarily for coding tasks.
- Requires Docker environment for optimal performance.
Why We Love It
- It sets new standards for open-source coding models by achieving state-of-the-art performance on real-world software engineering tasks with practical, deployable solutions.
Open Source LLM Comparison
In this table, we compare 2025's leading open source LLMs, each with unique strengths. For advanced reasoning tasks, DeepSeek-R1 offers unmatched performance comparable to OpenAI-o1. For versatile applications requiring both reasoning and dialogue, Qwen3-235B-A22B provides dual-mode flexibility. For specialized coding tasks, Kimi-Dev-72B delivers state-of-the-art software engineering capabilities. This comparison helps you choose the right model for your specific AI development needs.
| Number | Model | Developer | Subtype | SiliconFlow Pricing | Core Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning Model | Input: $0.50/M | Output: $2.18/M | Advanced reasoning capabilities |
| 2 | Qwen3-235B-A22B | Qwen3 | MoE Reasoning Model | Input: $0.35/M | Output: $1.42/M | Dual-mode flexibility |
| 3 | Kimi-Dev-72B | moonshotai | Coding Model | Input: $0.29/M | Output: $1.15/M | State-of-the-art coding performance |
Frequently Asked Questions
Our top three picks for 2025 are DeepSeek-R1, Qwen3-235B-A22B, and moonshotai/Kimi-Dev-72B. Each of these models stood out for their innovation, performance, and unique approach to solving challenges in reasoning, dialogue, and coding tasks respectively.
Our analysis shows different leaders for specific needs. DeepSeek-R1 excels in complex reasoning tasks comparable to OpenAI-o1. Qwen3-235B-A22B is ideal for applications requiring both reasoning and efficient dialogue with multilingual support. Kimi-Dev-72B is the top choice for software engineering and coding tasks with state-of-the-art performance on SWE-bench.