What are Open Source LLMs for Coding?
Open source LLMs for coding are specialized Large Language Models designed to understand, generate, and debug code across multiple programming languages. Using advanced deep learning architectures and trained on vast coding datasets, they translate natural language prompts into functional code, assist with debugging, and provide intelligent code completion. This technology allows developers to accelerate development workflows, automate routine coding tasks, and build sophisticated software engineering solutions with unprecedented efficiency. They foster collaboration, accelerate innovation, and democratize access to powerful coding assistance tools, enabling a wide range of applications from individual development to large-scale enterprise software engineering.
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. This ensures the model delivers correct, robust, and practical solutions aligned with real-world software engineering standards.
Kimi-Dev-72B: State-of-the-Art Software Engineering
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. This ensures the model delivers correct, robust, and practical solutions aligned with real-world software engineering standards. With 72B parameters and 131K context length, it excels at understanding large codebases and complex programming tasks.
Pros
- Achieves 60.4% on SWE-bench Verified - state-of-the-art among open-source models.
- Optimized through large-scale reinforcement learning for real-world coding.
- Autonomously patches real codebases with Docker integration.
Cons
- Large 72B parameter model requires significant computational resources.
- Higher pricing due to model complexity and performance.
Why We Love It
- It sets the gold standard for open-source coding models with proven real-world software engineering capabilities and benchmark-leading performance.
Qwen3-Coder-480B-A35B-Instruct
Qwen3-Coder-480B-A35B-Instruct is the most agentic code model released by Alibaba to date. It is a Mixture-of-Experts (MoE) model with 480 billion total parameters and 35 billion activated parameters, balancing efficiency and performance. The model supports repository-scale understanding with 256K context length and is specifically designed for agentic coding workflows.
Qwen3-Coder-480B-A35B-Instruct: The Ultimate Agentic Coding Model
Qwen3-Coder-480B-A35B-Instruct is the most agentic code model released by Alibaba to date. It is a Mixture-of-Experts (MoE) model with 480 billion total parameters and 35 billion activated parameters, balancing efficiency and performance. The model natively supports a 256K token context length, which can be extended up to 1 million tokens, enabling it to handle repository-scale codebases and complex programming tasks. Qwen3-Coder is specifically designed for agentic coding workflows, where it not only generates code but also autonomously interacts with developer tools and environments to solve complex problems.
Pros
- Most agentic coding model with 480B total parameters.
- Repository-scale understanding with 256K-1M token context.
- Autonomous interaction with developer tools and environments.
Cons
- Highest resource requirements among coding models.
- Premium pricing reflects advanced capabilities.
Why We Love It
- It represents the pinnacle of agentic coding AI, capable of autonomous software development workflows and repository-scale code understanding.
DeepSeek-V3
DeepSeek-V3 utilizes reinforcement learning techniques from the DeepSeek-R1 model, significantly enhancing its performance on reasoning and coding tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. The model features a Mixture-of-Experts architecture with 671B parameters and notable improvements in tool invocation capabilities.
DeepSeek-V3: Advanced Code Reasoning Powerhouse
The new version of DeepSeek-V3 (DeepSeek-V3-0324) utilizes the same base model as the previous DeepSeek-V3-1226, with improvements made only to the post-training methods. The new V3 model incorporates reinforcement learning techniques from the training process of the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. Additionally, the model has seen notable improvements in tool invocation, role-playing, and casual conversation capabilities.
Pros
- Surpasses GPT-4.5 on mathematics and coding evaluations.
- Enhanced reasoning capabilities through reinforcement learning.
- Improved tool invocation for coding workflows.
Cons
- Very high computational requirements for deployment.
- Complex architecture may require specialized expertise to optimize.
Why We Love It
- It delivers GPT-4.5-beating performance in coding tasks while maintaining open-source accessibility and advanced reasoning capabilities.
Coding AI Model Comparison
In this table, we compare 2025's leading open-source coding LLMs, each with unique strengths. For benchmark-leading software engineering, Kimi-Dev-72B provides state-of-the-art SWE-bench performance. For autonomous agentic coding workflows, Qwen3-Coder-480B-A35B-Instruct offers unmatched repository-scale capabilities, while DeepSeek-V3 prioritizes advanced reasoning and tool integration. This side-by-side view helps you choose the right coding assistant for your specific development needs.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Kimi-Dev-72B | moonshotai | Code Generation | $0.29-$1.15/M tokens | SWE-bench leader (60.4%) |
| 2 | Qwen3-Coder-480B-A35B-Instruct | Qwen | Agentic Coding | $1.14-$2.28/M tokens | Repository-scale understanding |
| 3 | DeepSeek-V3 | deepseek-ai | Code Reasoning | $0.27-$1.13/M tokens | GPT-4.5-beating performance |
Frequently Asked Questions
Our top three picks for 2025 are Kimi-Dev-72B, Qwen3-Coder-480B-A35B-Instruct, and DeepSeek-V3. Each of these models stood out for their innovation, coding performance, and unique approach to solving challenges in software engineering, agentic coding workflows, and code reasoning tasks.
Our analysis shows clear leaders for different needs. Kimi-Dev-72B is the top choice for software engineering tasks requiring real codebase patching and SWE-bench performance. For developers needing autonomous coding agents and repository-scale understanding, Qwen3-Coder-480B-A35B-Instruct excels. For advanced code reasoning and tool integration, DeepSeek-V3 delivers superior performance.