Key Takeaways

• Claude Opus 4.5 ranks as the top AI LLM for vibe coding, scoring 80.9% on SWE-bench Verified and excelling at multi-file refactoring and long-running autonomous tasks
• Claude Sonnet 4.5 offers the best balance of speed, quality, and cost at $3 per million input tokens, making it ideal for everyday coding and quick fixes
• GPT-5.2 Codex performs exceptionally well for frontend development and rapid prototyping, with strong planning and architecture capabilities
• Gemini 3 Pro dominates backend logic, database implementation, and competitive programming with the highest LiveCodeBench score (2,439 Elo)
• No single model handles every task perfectly—experienced developers combine multiple LLMs based on specific requirements
• For non-developers using vibe coding tools, the underlying model matters less than the platform (Lovable, Bolt, Replit) chosen for building applications

The current leader for vibe coding is Claude Opus 4.5, which became the first AI model to exceed 80% on SWE-bench Verified. This benchmark measures how well an AI can solve real GitHub issues from actual open-source projects—the closest proxy to genuine software engineering work. However, the “best” LLM depends entirely on your specific task: Claude Opus 4.5 handles complex multi-file operations with remarkable precision, GPT-5.2 Codex generates cleaner frontend code, and Gemini 3 Pro writes backend logic that requires minimal debugging.

Vibe coding has matured dramatically since Andrej Karpathy coined the term in early 2025. The practice involves describing software functionality in natural language and letting AI generate the code. What started as a novelty has become standard practice—approximately 41% of all new code is now written by AI, and 44% of non-technical founders build prototypes using AI coding tools.

AI LLM Tier List for Vibe Coding (January 2026)

The following rankings reflect real-world performance across production codebases, not just benchmark scores. Each model’s placement considers coding accuracy, token efficiency, and practical usability within vibe coding workflows.

S-Tier (Heavy Lifters)

A-Tier (Specialists)

B-Tier (Support Models)

Claude Opus 4.5: The Current Champion

Claude Opus 4.5 arrived in November 2025 with Anthropic claiming it as “the best model in the world for vibe coding, agents, and computer use.” Real-world testing supports this claim for complex projects.

The model’s defining characteristic is token efficiency. At medium effort settings, Opus 4.5 matches Sonnet 4.5’s best performance while using 76% fewer output tokens. At high effort, it exceeds Sonnet by 4.3 percentage points while still consuming 48% fewer tokens. This efficiency translates directly to cost savings for developers running extensive vibe coding sessions.

What makes Opus 4.5 particularly valuable for vibe coding is its ability to work autonomously for extended periods. Users report sessions stretching to 20-30 minutes without intervention, with tasks often completed correctly. The model excels at maintaining context across large codebases and coordinating changes across multiple files—a common failure point for other LLMs.

One developer noted after testing: “We haven’t been this enthusiastic about a coding model since Anthropic’s Sonnet 3.5 dropped in June 2024. The step up from Gemini 3 or even Sonnet 4.5 is significant. It’s less sloppy in execution, stronger visually, doesn’t spiral into overwrought solutions, holds the thread across complex flows, and course-corrects when needed.”

The main drawback is cost. Opus 4.5 commands premium pricing, and quota-conscious users may find themselves rationing requests. For simpler tasks, this premium delivers diminishing returns.

Claude Sonnet 4.5: The Practical Workhorse

Sonnet 4.5 occupies the sweet spot between capability and cost. Released in September 2025, it handles most vibe coding tasks with speed that makes Opus feel sluggish by comparison.

The model particularly shines for finishing work. After a long Opus session building major features, Sonnet 4.5 polishes the results—fixing minor bugs, tweaking UI elements, and generating changelogs. It performs best with strong context, so pointing it at specific elements after testing produces reliable improvements.

Speed matters in vibe coding workflows. Sonnet 4.5 completed comprehensive code reviews of large features in roughly two minutes during testing. This responsiveness makes iterative development more practical than waiting for heavier models.

Sonnet 4.5 scores 77.2% on SWE-bench Verified (82.0% with parallel compute), placing it among the top performers. On Terminal-Bench, it reaches 50.0%, ahead of both Opus 4.1 and GPT-5. For desktop and browser interaction (OSWorld benchmark), it achieves 61.4%—the current reference point for computer use tasks.

The tradeoff is depth. Sonnet occasionally hedges in complex scenarios where Opus would deliver confident solutions. About 34% of its actionable comments include qualifying language like “might” or “could,” compared to 28% for Opus.

GPT-5.2 Codex: The Architect and Frontend Specialist

OpenAI’s GPT-5.2 Codex takes a different approach to vibe coding. Where Claude models excel at implementation, GPT-5.2 demonstrates remarkable planning capabilities.

For system architecture, stack decisions, data modeling, and breaking complex ideas into actionable development steps, GPT-5.2 consistently produces excellent results. A single well-defined prompt often generates deeply structured plans with clear tasks, relevant sources, and strong reasoning chains.

The model also handles frontend development with notable grace. Websites, UI components, and visual elements emerge cleaner from GPT-5.2 than from competing models. Developers who describe what they want and expect the AI to handle details find GPT-5.2 delivers polished results.

However, direct vibe coding with GPT-5.2 produces more boilerplate and more bugs than Claude models. Independent analysis shows GPT-5.2 generates nearly three times the code volume of smaller models for identical tasks. This verbosity creates technical debt—the model tries to handle every edge case and adds safeguards that paradoxically make code harder to maintain.

GPT-5.2 achieved 80.0% on SWE-bench Verified (versus Opus’s 80.9%) and perfect 100% scores on AIME 2025 mathematical reasoning. Its strength lies in reasoning through problems rather than pure code generation.

Gemini 3 Pro: Backend Logic and Documentation

Gemini 3 Pro launched in November 2025 as Google’s most intelligent model, claiming state-of-the-art status across multiple benchmarks. For vibe coding, it occupies a specific niche: backend-focused development.

Database logic, authentication flows, and structured backend problems fall into Gemini’s wheelhouse. The model “gets it” quickly for these tasks, requiring minimal back-and-forth to reach solid implementations. Documentation and README generation also rank among its strengths.

On LiveCodeBench Pro (measuring algorithmic problem-solving), Gemini 3 Pro leads with a 2,439 Elo rating—roughly 200 points ahead of GPT-5.2. For competitive programming challenges, this represents genuine superiority. Google explicitly marketed it as “the best vibe coding and agentic coding model we’ve ever built.”

The model struggles with complex UI work. Production coding tests show Gemini 3 Pro landing in third place behind Claude Opus 4.5 and GPT-5.2 High for feature completeness and polish. Its outputs often feel like minimum viable implementations rather than refined products.

For developers comfortable doing frontend work themselves, Gemini 3 Pro handles the backend efficiently. The combination works particularly well when paired with other models for UI tasks.

Matching Models to Vibe Coding Tasks

Experienced vibe coders rarely stick to a single model. The workflow that produces the best results combines models strategically:

Complex Features and Multi-File Refactoring: Start with Claude Opus 4.5. Its context awareness and autonomous operation handle large changes without constant supervision. The 20-30 minute autonomous sessions allow focus on higher-level decisions while the model implements details.

Quick Fixes and Polishing: Switch to Claude Sonnet 4.5 after major features land. Speed matters for iteration, and Sonnet’s lower cost allows more experimental prompts without budget concerns.

Architecture and Planning: Use GPT-5.2 for initial project structure and breaking down requirements. Its planning capabilities translate vague ideas into concrete implementation roadmaps.

Backend Logic and Databases: Gemini 3 Pro writes clean backend code efficiently. Authentication, database schemas, and API logic require fewer corrections than with other models.

Quick Questions and Explanations: Claude Haiku 4.5 answers fast without consuming quota on heavier models. At one-third the token cost, it handles knowledge queries economically.

Vibe Coding Tools and Their Default Models

For non-developers using vibe coding platforms, the underlying LLM matters less than the tool’s overall design. These platforms handle model selection automatically:

• Cursor lets users choose between Claude, GPT, and Gemini models, making it ideal for developers who want to switch based on task requirements. Its Composer feature handles multi-file changes particularly well.
• Replit Agent uses its own AI trained for the platform’s workflow. The plan-first approach creates technical specifications before generating code, reducing errors in complex projects.
• Lovable and Bolt.new target non-technical users building MVPs. Both integrate with Supabase for backends and handle deployment automatically. Lovable produces more polished UI; Bolt offers more flexibility with frameworks.
• Claude Code operates from the terminal for developers comfortable with command-line workflows. It taps directly into Claude models and handles large codebases effectively.

Cost Considerations for Extended Vibe Coding

Token consumption during vibe coding sessions adds up quickly. Understanding model economics helps budget effectively:

Claude Opus 4.5’s token efficiency partially offsets its higher per-token price. Building features that would consume 100,000 tokens in Sonnet might only require 50,000 tokens in Opus, making the actual cost closer than pricing suggests.

GPT-5.2’s verbose output creates hidden costs. More tokens generated means higher bills, even at lower per-token rates. The model’s tendency toward comprehensive solutions inflates expenses for simple tasks.

Subscription plans offer predictability over API pricing. Claude Pro at $20/month or Claude Max at $100-200/month provides heavy users with more sustainable economics than pay-per-token models.

Limitations All Models Share

Despite impressive benchmarks, current LLMs have consistent failure modes in vibe coding:

• Context drift affects long sessions. Models lose track of earlier decisions, sometimes undoing features while implementing new ones. Periodic summaries and explicit reminders help maintain consistency.
• Security vulnerabilities appear in AI-generated code. Platforms catch obvious issues but miss subtle problems. Production deployments require human security review regardless of which model generated the code.
• Hallucinated APIs and methods still occur. Models confidently use functions that don’t exist or have different signatures than stated. Testing remains essential—AI-generated code is not inherently correct.
• Large codebases challenge all models. Projects exceeding 50,000 lines of code require careful context management. Modular architectures help models work on isolated components rather than attempting to understand everything simultaneously.

Practical Recommendations

For developers starting with vibe coding: begin with Claude Sonnet 4.5 for its balance of capability and cost. Upgrade to Opus 4.5 for complex features that justify the premium. Keep GPT-5.2 available for planning phases and frontend polish.

For non-developers building prototypes: choose Lovable or Replit based on interface preference. The underlying models matter less than learning to write effective prompts and iterate on AI-generated results.

For teams evaluating enterprise use: security review processes must accompany any vibe coding workflow. AI-generated code enters production only after the same quality gates applied to human-written code.

The vibe coding landscape continues evolving rapidly. Model capabilities that seem impressive today will likely appear limited within months. Building workflows that accommodate model switching ensures access to improvements as they arrive.

If you are interested in this topic, we suggest you check our articles:

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Sources: DEV, DreamHost, Hostinger, Reddit

Written by Alius Noreika