What LLM Does Replit Use? The Complete Guide to Replit's AI Models

Replit has transformed from a simple online coding platform into an AI-powered software creation hub. Millions of developers now rely on its intelligent features daily. But here's the question everyone asks: what LLM does Replit use to power its AI capabilities?

The answer might surprise you. Replit doesn't rely on just one language model. Instead, the platform strategically uses multiple LLMs across different features. These include Anthropic's Claude, Google's Gemini, and Replit's own open-source code models.

In this guide, you'll discover exactly which AI models power each Replit feature. You'll also learn why Replit chose this multi-model approach. Whether you're evaluating AI coding tools or just curious about the technology, this breakdown covers everything you need to know.

Quick Answer: Which AI Models Power Replit?

Before diving deep, here's a straightforward breakdown of every LLM Replit currently uses:

So, is Replit powered by ChatGPT? No, it isn't. The platform primarily relies on Claude by Anthropic for its flagship Agent feature. However, developers can access OpenAI's GPT-4o when building their own applications through Replit's AI Integrations.

This multi-model strategy sets Replit apart from competitors. Each AI model serves a specific purpose based on its strengths. Claude handles complex reasoning tasks. Gemini delivers speed for quick interactions. Replit's own model provides cost-effective code completion for free users.

Replit Agent: Powered by Anthropic's Claude Sonnet

The Replit Agent represents the platform's most powerful AI feature. It can build complete applications from natural language prompts. Behind this capability sits Claude Sonnet, Anthropic's advanced language model.

Why Replit Chose Claude for Its AI Agent

Replit evaluated several AI models before selecting Claude. The decision came down to performance on code generation and editing tasks. Claude excelled at both.

According to Michele Catasta, President of Replit: "Claude 3.5 Sonnet delivered such a jump in the ability to code and reason that Replit accelerated its product roadmap."

Several factors made Claude the ideal choice for Replit's agent:

• Superior code generation: Claude writes cleaner, more functional code than alternatives
• Request chaining: The model handles multiple operations simultaneously
• Self-correction: Claude debugs its own code without getting stuck in loops
• Complex reasoning: It understands project context and makes architectural decisions

The Replit AI model runs on Google Cloud's Vertex AI infrastructure. This partnership ensures reliable performance for millions of users worldwide.

How Claude Evolved Within Replit Agent

Replit hasn't stayed static with its AI model choices. The platform continuously upgrades to newer Claude versions:

• September 2024: Agent launched with Claude 3.5 Sonnet
• October 2024: Upgraded to enhanced Claude 3.5 Sonnet with computer use
• February 2025: Agent v2 released with Claude 3.7 Sonnet
• May 2025: Integration with Claude Opus 4 and Sonnet 4
• September 2025: Claude Sonnet 4.5 became the primary model

Each upgrade brought measurable improvements. With Claude Sonnet 4.5, Replit reported a code editing error rate drop from 9% to 0% on internal benchmarks.

The Multi-Agent Architecture Behind Replit Agent

Replit doesn't use Claude as a single monolithic agent. Instead, the platform built a sophisticated multi-agent system. Different agents handle specialized tasks:

• Manager Agent: Oversees the entire workflow and coordinates tasks
• Editor Agents: Handle specific coding operations and file modifications
• Verifier Agent: Checks code quality and interacts with users for feedback

This architecture improves reliability significantly. When one agent encounters an issue, others can compensate. The verifier agent specifically ensures users stay involved throughout the development process.

Replit also employs dynamic prompt construction techniques. These methods compress long conversation histories while retaining critical context. As a result, the AI maintains coherence across extended coding sessions.

Replit's Own Open-Source Code Language Models

While Claude powers the Agent, Replit developed proprietary models for other features. These open-source LLMs demonstrate the company's commitment to AI independence.

Why Replit Built Its Own LLMs

Training custom language models requires massive resources. So why did Replit invest in this capability? Three core reasons drove the decision:

Customization: Generic models don't understand Replit-specific patterns. Custom models excel at web languages popular on the platform, including JavaScript React (JSX) and TypeScript React (TSX).

Reduced Dependency: Relying solely on external providers creates risk. Building internal models gives Replit flexibility and control over its AI future.

Cost Efficiency: Replit's mission targets the next billion software creators. A student coding on a phone in India should access the same AI as a Silicon Valley developer. Smaller, efficient models make this financially possible.

replit-code-v1-3b: The First Release

In May 2023, Replit released its first open-source code model. Despite being 77% smaller than OpenAI's Codex, it delivered impressive results.

Key specifications of replit-code-v1-3b include:

• Parameters: 2.7 billion
• Training Data: 525 billion tokens of code
• Languages Supported: 20 programming languages
• HumanEval Score: 21.9% (30.5% after fine-tuning)
• Training Hardware: 256 NVIDIA A100-40GB GPUs via MosaicML

The model outperformed much larger alternatives on code completion tasks. Fine-tuning with Replit's proprietary data boosted performance even further.

replit-code-v1.5-3b: The Current Production Model

October 2023 brought an upgraded version with significant improvements. This Replit language model now powers free code completion for all users.

Notable upgrades in version 1.5 include:

• Parameters: 3.3 billion
• Training Data: 1 trillion tokens (nearly double the original)
• Languages Supported: 30 programming languages
• Training Hardware: 128 NVIDIA H100-80GB GPUs

This became the first model trained on H100 GPUs released as open source. Anyone can download it from Hugging Face for custom applications.

Technical Architecture Details

Replit's code models incorporate cutting-edge techniques:

• Custom Tokenizer: A SentencePiece vocabulary with 32,768 tokens optimized for code
• Flash Attention: Enables faster training and inference speeds
• AliBi Positional Embeddings: Supports variable context lengths during inference
• Grouped Query Attention: Improves efficiency without sacrificing quality

These architectural choices prioritize speed. Replit's IDE requires near-instant code suggestions. Latency directly impacts user experience.

Google Gemini Powers Replit Assistant and Design Mode

Claude handles complex Agent tasks, but Google's Gemini family serves other crucial roles. Replit leverages different Gemini models for speed-critical and design-focused features.

Gemini 1.5 Flash for Replit Assistant

Replit Assistant works alongside the main Agent. It helps users improve existing code, fix errors, and add new features. Speed matters most for this use case.

Gemini 1.5 Flash delivers exactly that. The model responds quickly to simple queries without the overhead of larger models. Users get instant help for routine coding questions.

According to Google Cloud's case study, Replit specifically chose Gemini Flash for its efficiency. The model handles high-volume, straightforward requests cost-effectively.

Gemini 3 Powers the New Design Mode

November 2025 introduced Design Mode, Replit's fastest way to create websites. This feature runs entirely on Google's newest Gemini 3 model.

Design Mode creates interactive mockups and static sites in under two minutes. The AI understands visual concepts that pure code models miss:

• Layout and spacing
• Color harmony and contrast
• Typography choices
• Visual hierarchy

Gemini 3 doesn't just generate code. It comprehends what looks aesthetically pleasing. Users describe their vision in natural language, and the AI produces polished designs instantly.

The feature serves as an alternative to tools like Figma Make or Magic Patterns. Product managers and designers can create clickable prototypes without engineering support.

Gemini 3 Integration for Advanced Coding

Google announced Replit as an integration partner for Gemini 3 Pro. This extends beyond Design Mode into coding workflows.

The collaboration enhances vibe coding capabilities. Users can generate rich, interactive web applications through conversational prompts. Gemini 3's improved instruction-following makes complex requests more reliable.

Replit Ghostwriter: Where the AI Journey Began

Before Claude and Gemini, Replit Ghostwriter pioneered the platform's AI features. Understanding its evolution explains how Replit's AI coding capabilities developed.

The Original Ghostwriter Launch

October 2022 marked Ghostwriter's public release. The feature introduced four core AI capabilities:

• Complete Code: Inline suggestions while typing
• Explain Code: Step-by-step breakdowns of any code block
• Transform Code: Refactoring based on natural language instructions
• Generate Code: Creating functions from text descriptions

Originally, Ghostwriter relied on models based on Salesforce's CodeGen architecture. Replit optimized these through knowledge distillation, creating faster 1-billion parameter variants.

Evolution Into Replit AI

As AI became central to the platform, Ghostwriter transformed. In October 2023, Replit integrated these features into the core product. The company dropped the Ghostwriter name, simply calling it "Replit AI."

Today, the original Ghostwriter capabilities persist. However, different models now power different functions:

• Code completion uses replit-code-v1.5-3b for free users
• Premium features leverage Claude and other advanced models
• Semantic search employs a fine-tuned CodeBERT model

This evolution reflects Replit's pragmatic approach. The platform matches each task with the most suitable AI model.

Replit AI Integrations: Access Multiple LLMs for Your Apps

Replit doesn't just use AI internally. The platform also provides developers access to external LLMs through AI Integrations.

How AI Integrations Work

Building AI-powered applications typically requires managing API keys and credentials. Replit AI Integrations eliminates this friction.

The feature provides managed access to major AI providers:

• OpenAI: GPT-4o, o3, and other models
• Anthropic: Claude model family
• Google: Gemini models
• OpenRouter: Access to Meta Llama, Mistral, DeepSeek, and more

Replit handles credential management automatically. Developers simply describe what they want, and the Agent builds using the appropriate API.

Billing and Usage

Usage charges appear on your Replit account at public API prices. No separate billing relationships with AI providers are necessary. This simplification accelerates development significantly.

Alternatively, developers can provide their own API keys. This option suits those with existing provider relationships or specific pricing arrangements.

Common Use Cases

Developers leverage AI Integrations for numerous applications:

• Chatbots and conversational interfaces
• Content generation tools
• Document analysis systems
• AI-powered automation workflows

The feature democratizes AI development. Even beginners can incorporate advanced language models into their projects without complex setup.

Why Does Replit Use Multiple LLMs?

Some platforms commit to a single AI provider. Replit deliberately chose a different path. Understanding this strategy reveals important insights about AI model selection.

Task-Specific Model Optimization

Different language models excel at different tasks. Claude demonstrates superior reasoning for complex coding challenges. Gemini Flash delivers speed for simple queries. Replit's own model provides cost-effective basic completion.

Matching models to tasks improves overall performance. Users experience better results than any single-model approach could deliver.

Cost Management at Scale

Advanced AI models like Claude cost significantly more than simpler alternatives. Using Claude for every autocomplete suggestion would be prohibitively expensive.

Replit's tiered approach reserves expensive models for high-value tasks. Basic code completion uses the efficient proprietary model. This balance enables free AI features while maintaining quality where it matters most.

Provider Independence

Depending entirely on one AI company creates vulnerability. Model availability, pricing changes, or performance degradation could cripple dependent platforms.

Replit's multi-provider strategy mitigates these risks. If one provider encounters issues, alternatives exist. The company can also adopt superior models as they emerge from any provider.

Michele Catasta summarized this philosophy: "We'll always use the right model based on the task at hand."

Replit vs Competitors: How Do the AI Models Compare?

Understanding what AI powers Replit helps when comparing alternatives. Here's how Replit's approach stacks against major competitors.

Replit vs Cursor

Cursor has gained popularity as an AI-first code editor. The tool primarily uses Claude and GPT-4 for its features.

For professional developers comfortable with traditional workflows, Cursor excels. For end-to-end development with deployment, Replit provides more value.

Replit vs GitHub Copilot

GitHub Copilot pioneered AI code completion. It runs on OpenAI's Codex and GPT-4 models.

Copilot suits developers who want AI assistance in familiar tools. Replit serves those wanting an all-in-one platform from idea to deployed application.

Replit Agent vs Lovable

Lovable emerged as a direct Replit Agent competitor. Both platforms use Claude to build full-stack applications from prompts.

Choosing between them depends on priorities. Lovable excels at rapid MVP creation. Replit provides a more complete development ecosystem.

The Evolution of Replit's AI: A Complete Timeline

Replit's AI capabilities developed rapidly over recent years. This timeline tracks every major milestone.

2022

• October: Ghostwriter launches publicly with CodeGen-based models

2023

• April: Replit announces internal LLM training capabilities
• May: Releases replit-code-v1-3b as open source on Hugging Face
• October: Launches replit-code-v1.5-3b; makes AI free for all users

2024

• June: Begins Claude 3.5 Sonnet integration and testing
• September: Replit Agent launches powered by Claude 3.5 Sonnet
• October: Upgrades to enhanced Claude 3.5 Sonnet with computer use capability

2025

• February: Agent v2 releases with Claude 3.7 Sonnet
• May: Integrates Claude Opus 4 and Sonnet 4
• September: Adopts Claude Sonnet 4.5 as primary Agent model
• November: Launches Design Mode powered by Gemini 3

This progression shows Replit's commitment to continuous improvement. The platform consistently adopts cutting-edge models as they become available.

Frequently Asked Questions About Replit's AI Models

Is Replit Powered by ChatGPT?

No, Replit does not use ChatGPT for its core features. The Replit Agent runs on Claude Sonnet by Anthropic. However, developers can access OpenAI's GPT-4o through AI Integrations when building their own applications.

What AI Model Does Replit Agent Use?

Replit Agent currently uses Claude Sonnet 4.5 by Anthropic. The model runs on Google Cloud's Vertex AI infrastructure. This represents the latest in a series of Claude upgrades since the Agent launched in September 2024.

Does Replit Have Its Own AI Model?

Yes, Replit developed and released replit-code-v1.5-3b. This open-source model has 3.3 billion parameters trained on 1 trillion tokens. It powers free code completion features and is available on Hugging Face for anyone to use.

Can I Use GPT-4 With Replit?

Absolutely. Replit AI Integrations provides managed access to OpenAI models including GPT-4o. When building AI-powered applications, you can leverage GPT-4 without managing API keys yourself.

What Makes Replit's AI Different From Cursor or Copilot?

Replit uses multiple AI models optimized for different tasks. Claude handles complex agent operations. Gemini provides speed for assistant features. The proprietary model enables free code completion. Competitors typically rely on a single provider. Additionally, Replit includes deployment capabilities that pure code assistants lack.

Is Replit's AI Free to Use?

Basic AI features including code completion are free for all Replit users. Advanced Agent capabilities require paid plans starting at $25 per month. The free tier uses Replit's efficient proprietary model to keep costs manageable.

Conclusion: Replit's Multi-Model AI Strategy

So what LLM does Replit use? The answer isn't simple because Replit made a strategic choice. Rather than committing to one provider, the platform leverages multiple AI models.

Claude Sonnet powers the flagship Agent feature. Gemini handles Assistant queries and Design Mode visuals. Replit's open-source model delivers free code completion to millions.

This approach optimizes performance, manages costs, and reduces provider dependency. Users benefit from each model's strengths without experiencing their limitations.

For developers evaluating AI coding platforms, understanding these details matters. Replit's multi-model strategy represents a thoughtful approach to AI integration. The platform continues evolving as new models emerge.

Whether you're a beginner building your first app or a professional seeking AI acceleration, Replit's AI ecosystem offers compelling capabilities. The combination of Claude's reasoning, Gemini's speed, and accessible free features creates a powerful development environment.

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Understanding AI models is just the beginning. Turning that knowledge into production applications requires the right development partner.

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