What Is Vibe Coding?
Vibe coding is an AI-assisted programming approach where you describe what you want in plain English and let AI generate the code. The term was coined by Andrej Karpathy in February 2025, describing it as a new kind of coding where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.
The concept gained such traction that it was named the Collins Dictionary Word of the Year for 2025. Unlike traditional programming, vibe coding shifts the developer role from writing code to guiding, testing, and iterating on AI-generated output.
Vibe Coding Is
- Describing apps in natural language
- Letting AI generate complete code
- Iterating through conversation
- Testing and refining output
- Shipping 3-5x faster
- Accessible to non-developers
Considerations
- Code review still recommended
- Security requires explicit prompts
- Complex systems need human oversight
- Not ideal for performance-critical code
- Context limits on large codebases
- Best for MVPs and prototypes
"There's a new kind of coding I call vibe coding, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists."
— Andrej Karpathy, February 2025
AI Model Comparison for Vibe Coding
Based on LM Council benchmarks and Artificial Analysis data, here is how the leading AI models compare for vibe coding tasks in January 2026.
| Model | SWE-bench | WebDev Arena | Context | Best For |
|---|---|---|---|---|
Claude 4.5 OpusTop Pick | 82.0% | #1 (32k) | 200K tokens | Complex debugging, clean code |
| GPT-5 (Thinking) | 74.9% | High | 128K tokens | Complex reasoning, architecture |
| Gemini 3 Pro | ~67% | 1487 Elo | 1M tokens | Web apps, multimodal tasks |
| Claude Sonnet 4.5 | 77.2% | High | 200K tokens | Balanced speed and quality |
| DeepSeek V3.2 | ~70% | Good | 128K tokens | Open source, self-hosting |
Sources: Anthropic, OpenAI, Google DeepMind
Claude (Anthropic) — Best for Complex Coding
Claude from Anthropic is widely considered the best AI for vibe coding among professional developers. According to PlayCode research, most professional programmers prefer Claude, especially Claude 4.1 and newer, as their top coding assistant.
SWE-bench Verified (Sonnet 4.5)
Terminal-bench score
Code editing error rate (down from 9%)
Thinking Mode
Claude's Thinking mode allows it to plan architecture before writing code. Unlike models that rush to solutions, Claude maps out dependencies first, leading to fewer bugs in complex React or Python projects.
Large Codebase Understanding
Claude excels at understanding long files, following relationships between different parts of a project, and explaining its answers clearly. Navigation errors dropped from 20% to near zero.
Best Use Cases for Claude
GPT-5 (OpenAI) — Best for Reasoning & Speed
GPT-5 from OpenAI leads the Artificial Analysis Intelligence Index with 50 points, making it the top overall benchmark performer. Its strength lies in complex reasoning and rapid iteration.
SWE-bench Verified (Thinking)
Aider Polyglot benchmark
Intelligence Index v4.0
Source: OpenAI GPT-5 Announcement
Speed & Flexibility
GPT-4.1 and successors are built for speed and flexibility. They excel at writing new functions, drafting components, or fleshing out ideas quickly with minimal latency.
Complex Reasoning
For complex architectural decisions, GPT-5 (xhigh) is recommended. Both Claude and GPT-5 score above 85% on LiveCodeBench and excel at multi-file code understanding.
Best Use Cases for GPT
Gemini (Google) — Best for Web Dev & Context
Gemini from Google ranks #1 on the WebDev Arena leaderboard, measuring human preference for building aesthetically pleasing and functional web apps. Its massive 1 million token context window is 5x larger than competitors.
Token context window
WebDev Arena Elo (2.5 Pro)
LMArena user preference
Live Camera Debugging
Gemini's live camera feature lets AI see your computer screen in real-time. This is especially useful while vibe coding to ask it to debug visual issues as they appear.
Entire Codebase Context
The 1 million token context window allows Gemini to comprehend entire codebases at once — 5x more than Claude's 200K, enabling better understanding of large projects.
Best Use Cases for Gemini
Open Source Alternatives
For developers who prefer self-hosting or want more control, open source models have reached impressive capabilities. According to O-Mega AI research, models like DeepSeek V3.2 and Llama 4 now match GPT-4 level performance.
DeepSeek V3.2
MIT License685B total parameters (37B active per token) using MoE architecture. Beats GPT-5 on reasoning tasks and supports 300+ programming languages.
Requirement: 48-80GB VRAM for optimal performance
Llama 4 (Meta)
Community LicenseScout and Maverick variants outperform GPT-4o and Gemini 2.0 Flash on many benchmarks. Commercial use allowed under 700M MAU.
Note: Requires "Built with Llama" branding for commercial use
Qwen3 (Alibaba)
Open Source1 trillion+ parameters via MoE, supports 119 languages with 92.3% accuracy on AIME25. Meets or beats GPT-4o while using far less compute.
Strength: Best multilingual support
Gemma 3 (Google)
Open Weights27B model that outperforms Llama-405B and DeepSeek-V3 on LMArena benchmarks. Built using tech from Gemini 2.0.
Best for: Efficient local deployment
How to Choose the Right AI for Your Project
According to Fello AI analysis, there is no single best AI for everything in January 2026. The right choice depends on your specific task, project size, and workflow preferences.
What type of project are you building?
Quick Decision Matrix
| If You Need... | Choose | Why |
|---|---|---|
| Best code quality & debugging | Claude 4.5 | 82% SWE-bench, 0% error rate |
| Complex reasoning tasks | GPT-5 | #1 Intelligence Index |
| Web app development | Gemini 3 | #1 WebDev Arena |
| Large codebase work | Gemini 2.5+ | 1M token context |
| Rapid prototyping | GPT-4.1 | Fast, flexible |
| Self-hosting | DeepSeek V3.2 | MIT license, GPT-5 level |
| Mobile app generation | Multi-model (Natively) | Combines strengths |
Multi-Model App Building with Natively
The most effective approach to vibe coding often combines multiple AI models. Platforms like Natively leverage this multi-model strategy to generate complete native mobile applications from text descriptions.
How AI App Builders Combine Models
1. Describe
Explain your app idea in plain English. No technical knowledge required.
2. AI Generates
Multiple AI models work together to create React Native code, UI, and backend.
3. Deploy
One-click deployment to iOS App Store and Google Play with full code ownership.
Why Multi-Model Works Better
- Claude for clean, documented code structure
- Specialized models for UI generation
- Reasoning models for complex logic
- Optimized models for specific tasks
Natively Features
- Native iOS & Android apps (React Native)
- Full source code ownership
- Supabase backend included
- Starting at $5/month
Ready to Start Vibe Coding?
Skip the model selection paralysis. Describe your app idea and let Natively handle the AI orchestration to build your native mobile app.
Build Your App NowNo coding required • Full code ownership • Deploy to app stores
Frequently Asked Questions
Which AI is best for vibe coding in 2026?
For vibe coding in 2026, Claude 4.5 Opus is widely considered the best choice for complex coding tasks, scoring 82% on SWE-bench Verified. GPT-5 leads in reasoning tasks, while Gemini 3 Pro excels at web development and offers a 1 million token context window. The best choice depends on your specific needs: Claude for clean code and debugging, GPT-5 for complex reasoning, and Gemini for web apps and visual tasks.
What is vibe coding?
Vibe coding is an AI-assisted software development approach where you describe what you want in natural language and let AI generate the code. Coined by Andrej Karpathy in February 2025, it means fully embracing AI assistance and focusing on guiding, testing, and iterating rather than writing code manually. It was named Collins Dictionary Word of the Year 2025.
Is Claude or GPT better for coding?
Claude tends to produce cleaner, more documented code and excels at understanding large codebases. Claude Sonnet 4.5 scores 82% on SWE-bench, slightly ahead of GPT-5 at 74.9%. However, GPT-5 leads in complex reasoning tasks. Most professional programmers prefer Claude for coding, while GPT excels at quick prototyping and creative solutions.
Can I use open source AI models for vibe coding?
Yes, open source models like DeepSeek V3.2 (685B parameters) and Llama 4 are viable for vibe coding. DeepSeek matches GPT-5 on reasoning benchmarks and is available under MIT license. However, they require significant hardware (48-80GB VRAM for best results) and may lack the polish of commercial options.
How do AI app builders like Natively use these AI models?
AI app builders like Natively leverage multiple AI models to generate complete applications from text descriptions. They combine the strengths of different models - using Claude for code quality, reasoning models for complex logic, and specialized models for UI generation. This multi-model approach produces better results than any single model alone.