Let’s look at the best proprietary and open-source AI models for coding in 2025. We’ll list easy-to-use solutions that offer a user-facing interface, like a chatbot or a web app, so you can start coding with AI right away.
The best AI models for coding (in no particular order) are:
1. Claude 3.7 Sonnet
Anthropic’s Claude is an AI assistant available via a web chat interface on Overchat AI. The latest Claude model — Claude 3.7 Sonnet excels at coding. Claude 3.7 outperforms ChatGPT 4.5/4.1, though falls behind compared to deep reasoning models like DeepSeek and GPT o1/o3.
Claude often produces more correct code on the first try, compared to GPT-4, and might have better support for less known languages, live Svelte. Claude supports large prompts up to 100k tokens, which is helpful for working with large code bases.
💡 When to use Calude?
When you need to generate a larger code artifact, like a component for a big application, or create a simple app with one prompt. Fun fucat: Cursor code editor uses Claude by default.
2. ChatGPT 4.1
ChatGPT is a chat-based AI assistant GPT-4 line of models that produces high-quality code at the level of a junior developer. It can also explain or debug code. The model handles Python, JavaScript, C++, Java, C#, and other languages.
GPT-4.1 released in April 2025 is particularly worth highlighting. It scored 54.6% on SWE-bench Verified, which is a 21.4% absolute gain over GPT-4o, and on some benchmarks it even outperforms GPT-4.5. GPT-4.1 has deep chain-of-thought and a massive context window (1M tokens) for handling large codebases.
💡When to use ChatGPT?
When you need to generate scripts, get AI answers about coding questions, or learn to code. You can try GPT 4.1 for free without login on Overchat AI.
3. Google Gemini
Google’s Gemini line of models can generate, debug, and explain code. Gemini can create working code snippets and explain logic, but it is oriented towards conversational queries rather than an in-IDE assistant. However, it’s generally very good for prototyping and learning how to code.
Google’s Gemini 2.5 Pro is the most powerful model in the range. In SWE-bench Verified Gemini 2.5 Pro scored 63.8%. However, this was as an automated agent — when used as a chatbot this score will drop by at least 5%.
Nevertheless, tThis is one of the highest scores ever. Gemini 2.5 Pro has a 1 million token context window and the ability to expand it to 2M, so it to ingest entire codebases. Gemini also knows more than 20 coding languages, among them C++, Go, Java, JavaScript, Python, and TypeScript.
💡When to use Google Gemini?
You can simply type prompts and get code or explanations. Gemini is great at generating code snippets on-the-fly, learning, or answering coding questions. You can test Gemini 2.0 code generation on Overchat AI.
4. Llama 4 Scout and Maverick
Llama 4’s Scout and Maverick models are also solid for code generation. Maverick achieved 43.4% on LiveCodeBench, beating GPT-4o. Meta says Maverick is as good as GPT-4.5 and Claude 3.7 on benchmarks.
LLama can generate code in Python, Java, JavaScript, C++, and many more languages, though, as usual, it shows higher accuracy with mainstream languages — it will produce better React code than Svelte code.
💡When to use LLama 4?
On coding tasks, Llama 4 is competent but not record-breaking. One of its advantages is the ability to deploy it locally and run the model for free, though this requires technical knowledge and a powerful system. Alternatively, you can use LLama 4 online via our chatbot interface.
5. DeepSeek V3
DeepSeek V3, at the time of its release, outperformed all open-source models on code benchmarks, performing as good as close-sourced models from all the big tech companies. Even today it’s one of the most advanced models when it comes to accuracy and problem-solving. The downside? It takes longer to generate code.
💡When to use DeepSeek V3?
When you need a very low error rate and don’t mind waiting 2-3x longer for the model to finish writing your code.
Learn more about Deep Seek V3 →
6. GitHub Copilot
GitHub Copilot is an AI pair-programmer that works with code editors. As a context-aware tool, it can read your entire code base — this increases accuracy. For example, if you ask it to create a React component it will know how to correctly set it up in relation to your other components, or how to use your existing hooks within that component.
Copilot knows Python, JavaScript/TypeScript, Java, C++, C#, Go, Ruby, Rust, HTML/CSS, PHP, and many more languages.
It works as an IDE extension, inserting suggestions inline as you type, and you accept suggestions with keystrokes. This makes it very easy to use, but you need to have basic programming knowledge to take advantage of the tool.
💡When to use Copilot?
You already know how to code and want to speed up your work or improve code quality. You want to generate boilerplate much faster.
However, with Copilot Chat you can also generate code from scratch or ask AI questions about your existing code. GibHub Copilote is a paid service, and comes bundled with some GitHub plans.
7. Amazon Q Developer
Q Developer AI code assistant integrates into AWS Cloud9, VS Code, JetBrains, and other code editors. The AI has access to your entire code base and provides code suggestions as you write. You need more coding knowledge to use it, compared to conversational Chatbots, but it can be more accurate in some cases.
It supports Java, Python, JavaScript and TypeScript, C#, Go, PHP, Rust, Kotlin, and SQL, among others, as well as formats like JSON, YAML, HCL (Terraform), and AWS CDK for IaC
💡When to use Amazon Q Developer?
When you need code autocompletion and to generate boilerplate, and when you can take advantage of its built-in security scanning. Q Developer offers a free tier for individuals and is integrated into the AWS ecosystem.
Bottom line
At the end of the day every AI model on this list is very capable.
Tip: Don’t get too hung up on benchmarks. While benchmarks give us a good idea how each model performs, they’re also not the only source of truth.
💡A model with a later release date can produce fewer mistakes when generating code for a newer or lesser known library, than a model that’s more powerful on paper. That’s because the newer model might have documentation and code examples in its training dataset that a bigger, but older model does not.
The best way to figure out which AI model to use for coding is to try them all on your specific real-world use case. Thankfully, with Overchat AI it’s very easy to do, as we have online chatbots powered by most of the models on this list.
Browse models on Overchat AI →