Best 31 AI code generation Tools of 2025

Code2.AI is an innovative online platform that helps developers rapidly convert their ideas into code using artificial intelligence technology. The platform compresses code repositories to allow AI to understand and collaborate with developers in coding. Key advantages of Code2.AI include accelerating development processes, limitless coding capabilities, and seamless integration with existing projects. It supports any programming language, provides complete function code rather than mere snippets, and offers comprehensive user guides to enhance the effectiveness of AI in programming.

Yi-Coder is a series of open-source large language models (LLMs) that deliver state-of-the-art coding performance with fewer than 10 billion parameters. It comes in two sizes—1.5B and 9B parameters—offering base and chat versions designed for efficient inference and flexible training. Yi-Coder-9B has been additionally trained on 24 trillion high-quality tokens from GitHub's code repository-level code corpus and code-related data filtered from CommonCrawl. Yi-Coder excels in various programming tasks, including basic and competitive programming, code editing, repository-level completion, long-context understanding, and mathematical reasoning.

Llama Coder is an AI-based code generator powered by Llama 3.1 and Together AI. It comprehends user ideas and translates them into actual application code, significantly enhancing development efficiency and innovation speed. With robust AI model support, it boasts a high degree of intelligence and flexibility, representing a revolutionary technology in the programming field.

Mamba-Codestral-7B-v0.1 is an open-source code model developed by the Mistral AI Team based on the Mamba2 architecture. It has comparable performance to cutting-edge Transformer-based code models. It performs well in multiple industry-standard benchmark tests and provides advanced code generation and comprehension capabilities, suitable for programming and software development.

Codestral Mamba is an advanced code generation language model developed by Mistral AI team based on Mamba2 architecture. It features the benefits of linear time inference and the ability to theoretically model infinitely long sequences. The model has been professionally trained and possesses advanced code and inference capabilities that can compete with the current state-of-the-art Transformer-based models.

CodeGeeX4-ALL-9B is the latest open-source version of the CodeGeeX4 series of models. Based on continuous training of GLM-4-9B, it has significantly enhanced code generation capabilities. It supports functions such as code completion, generation, code interpretation, web search, function calling, and code question answering, covering multiple scenarios in software development. It performs excellently on public benchmark tests such as BigCodeBench and NaturalCodeBench, becoming the strongest code generation model with less than 10 billion parameters, achieving the best balance between inference speed and model performance.

Meta Large Language Model Compiler (LLM Compiler-13b-ftd) is an advanced large language model based on Code Llama, focused on compiler optimization and code reasoning. It exhibits exceptional performance in predicting LLVM optimization effects and assembly code decompilation, enabling significant improvements in code efficiency and size reduction.

LLM Compiler-7b-ftd is a large language model developed by Meta, based on Code Llama, which has been fine-tuned for code optimization and compiler inference. It excels in predicting LLVM optimization effects and can perfectly simulate compiler outputs, making it an ideal tool for compiler optimization tasks.

DeepSeek-Coder-V2 is an open-source expert mixture model (MoE) specifically designed for code language. Its performance is comparable to GPT4-Turbo. It excels in code-specific tasks while maintaining strong performance in general language tasks. Compared to DeepSeek-Coder-33B, the V2 version demonstrates significant improvements in code-related tasks and reasoning capabilities. Furthermore, it expands its supported programming languages from 86 to 338 and increases the context length from 16K to 128K.

DeepSeek-Coder-V2 is an open-source Mixture-of-Experts (MoE) code language model with performance comparable to GPT4-Turbo, showcasing exceptional performance in code-specific tasks. Built upon DeepSeek-Coder-V2-Base, it has undergone further pre-training using a high-quality, multi-source corpus of 6 trillion tokens. This has significantly enhanced its coding and mathematical reasoning capabilities while maintaining its performance on general language tasks. Supported programming languages have expanded from 86 to 338, and the context length has increased from 16K to 128K.

AutoCoder is a novel model specifically designed for code generation tasks. It outperforms GPT-4 Turbo (as of April 2024) and GPT-4-o on the HumanEval benchmark dataset. Unlike previous open-source models, AutoCoder introduces a new feature: it can automatically install required packages and attempt to run the code when the user wishes to execute it, ensuring it works without issues.

Codestral-22B-v0.1 is a large language model developed by Mistral AI Team. It has been trained on 80+ programming languages, including Python, Java, C, C++, JavaScript, and Bash. The model can generate code based on instructions, or explain and refactor code snippets. It also supports Fill in the Middle (FIM) functionality, used to predict the middle part of code, especially suitable for software development tool plugins, such as VS Code. The model currently does not have a content moderation mechanism, but the development team is seeking community collaboration to enable deployment in environments requiring content moderation.

Codestral, the first code generation AI model from the Mistral AI team, empowers developers to write and interact with code through shared instructions and completion API endpoints. Trained on over 80 programming languages, including Python, Java, C, C++, JavaScript, and Bash, Codestral can perform coding tasks, generate tests, and utilize a middle-fill mechanism to complete portions of code. Codestral sets a new performance standard with its 32k context window, surpassing all other models on RepoBench, which is significantly larger than competitors' 4k, 8k, or 16k windows. It offers a dedicated API endpoint, codestral.mistral.ai, allowing users to integrate it into their IDEs using Instruct or Fill-In-the-Middle routing and provides a free beta period of 8 weeks. Codestral is also integrated into popular application frameworks like LlamaIndex and LangChain, as well as development environments such as VSCode and JetBrains, enabling developers to generate and interact with code seamlessly within these platforms.

Infra Copilot for Infrastructure as Code (IaC), abbreviated as Infra Copilot, is a tool that uses machine learning technology to help infrastructure professionals automatically generate accurate infrastructure code. By understanding the context of infrastructure tasks, it allows professionals to use natural language to express their requirements and receive corresponding code suggestions. Infra Copilot not only simplifies the development process of Infrastructure as Code (IaC) but also ensures consistency across environments and projects, accelerates the onboarding and learning process for new team members, significantly improves work efficiency, and saves time.

CodeGemma is an advanced large language model released by Google, specializing in generating, understanding, and tracking instructions for code. It aims to provide global developers with high-quality code assistance tools. It includes a 2 billion parameter base model, a 7 billion parameter base model, and a 7 billion parameter model for guiding tracking, all optimized and fine-tuned for code development scenarios. It excels in various programming languages and possesses exceptional logical and mathematical reasoning abilities.

aiXcoder-7B is a 7 billion parameter code giant model designed for enterprise-grade software development. Its performance surpasses the 340 billion parameter Codellama model and performs exceptionally well in real development scenarios. It supports algorithm problem solving and complex multi-file code scenarios, generating complete code blocks and preferring concise code, thus enhancing maintainability and code quality. It is open-source and supports private deployment.

Stability AI has announced the release of Stable Code Instruct 3B, a large language model specifically designed to understand and execute code-related instructions. This model aims to help developers write, review, and optimize code more efficiently, boosting software development productivity.

Design2Code is a tool exploring the possibilities of automated frontend engineering. It aims to convert designs into code, enhancing both development efficiency and accuracy.

StarCoder2 is a 150 billion parameter Transformer model pretrained on over 600 programming language datasets, including GitHub. It utilizes technologies such as Grouped Query Attention. This model is suitable for code generation tasks and supports multiple programming languages.

OpenCodeInterpreter is an open-source code generation system that seamlessly combines code creation, execution, and iterative optimization. Trained using a dataset of 68,000 interactive Code-Feedback samples, it dynamically optimizes code based on execution outputs and human feedback. Its exceptional performance in code generation is highlighted by evaluations in benchmark tests like HumanEval and MBPP, where it achieves an average accuracy of 83.2% on OpenCodeInterpreter, which is comparable to the 84.2% of GPT-4 with an Improvement strategy and can be enhanced by human feedback to an accuracy of 91.6%. OpenCodeInterpreter narrows the gap between open-source code generation models and proprietary systems like GPT-4.

AlphaCodium is a test-based, multi-stage, code-centric iterative flow method aimed at enhancing the performance of LLMs on code-related problems. It optimizes model performance in code generation tasks, particularly for competitive programming. Users can select appropriate models (e.g., "gpt-4", "gpt-3.5-turbo-16k") based on their configuration and leverage AlphaCodium to tackle specific problems or entire datasets. The tool also provides a set of best practices, including YAML structured output, semantic reasoning, and modular code generation, which are applicable to various code generation tasks.

Stable Code 3B is a decoder-only language model with 2.7 billion parameters, pre-trained on 130 billion diverse text and code data points. Stable Code 3B was trained on 18 programming languages and demonstrated state-of-the-art performance compared to models of similar size on various programming languages when evaluated using the BigCode benchmark. It supports long contexts, trained with a sequence length of 16,384, and incorporates the Fill-in-the-Middle (FIM) technique. Users can begin using Stable Code 3B for text generation via code snippets available on the Hugging Face website. Developed by Stability AI based on the GPT-NeoX library, this model is applicable to both English and programming languages.

Ant CodeAI leverages OpenAI and Gemini technologies to generate high-quality, usable code. It supports web development (React, Vue, Tailwind CSS), native (React Native), and other codebases. Utilizing GPT-4 Vision, code generation methods include screenshots, drawing sketches, and inputting ideas.

vx.dev is an open-source alternative to v0.dev with the following benefits: - Cost-effective: Through prompt engineering, the cost of use can be greatly reduced - Easily customizable: Open-source prompts are provided, allowing for the customization of UI components or coding styles to meet specific needs - Seamless GitHub Integration: Generated code is stored on GitHub, featuring built-in version control and code review functionalities The working principle of vx.dev involves using the GPT-4 model to generate code based on predefined prompts. The main cost lies in the number of tokens for input and completion. Prompts are stored in 'prompts/ui-gen.md', including instructions for shadcn/ui, lucide, and nivo charts. Reducing unnecessary component instructions can lower the API cost of each generation. vx.dev is easy to customize. Users can modify existing prompts, switch to other UI libraries, or adjust the coding style. Generated code is stored on GitHub, offering version control and collaboration features. Private repositories ensure the visibility of generated results.

gpt-engineer is a code generation tool based on GPT-3 that requires no coding. Users only need to describe the desired functionality in natural language to generate the corresponding code implementation. It is very suitable for rapid prototyping and can significantly improve coding efficiency. The biggest advantage of gpt-engineer is that it is completely code-free, users do not need to learn any language to generate various code implementations. It also has the ability to remember and continuously improve code quality, which can continuously optimize code quality based on user requirements. Overall, gpt-engineer is a very promising new programming tool.

CodeGeeX2 is the second-generation model of the multilingual code generation model CodeGeeX._based on the ChatGLM2 architecture, it has significantly improved in both code generation capabilities and model deployment performance. It supports over 100 programming languages for functionalities such as code completion, code generation, code explanation, and document generation.

Generative UI is a product that can generate code from your description of the interface you want to build, helping developers quickly build website interfaces. It uses open-source tools like React, Tailwind CSS, and Shadcn UI to generate code and supports features such as customized design systems, themes, and image-to-code conversion. Generative UI helps developers quickly build website interfaces by providing a simple description interface and code generation, making it suitable for various project types. It offers free and paid plans, with paid plan users able to purchase more generation times as needed. Generative UI is the starting point for the next generation of user interfaces and will add more features and security controls in the future.

Screenshot-to-code is a simple application that uses GPT-4 Vision to generate code and DALL-E 3 to generate similar images. The application has a React/Vite frontend and a FastAPI backend. You will need an OpenAI API key with access to the GPT-4 Vision API.

Code Llama is an advanced large language model that can generate code from text prompts. It is one of the best-performing models in publicly available language models for programming tasks. Code Llama can help developers improve work efficiency, lower the coding threshold, and serve as an educational tool to help programming learners write more robust and well-documented software. Code Llama offers multiple versions, including a base version, a specialized version for Python, and a customized version for natural language instructions. It supports various popular programming languages such as Python, C++, Java, and more. Code Llama is freely available for research and commercial use.