Which LLM fits your Replit build? (Beginner “pick the right API” guide)

Tips & Tricks
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If you’re new, the fastest way to not get stuck is to pick an LLM based on what your app does, not what’s trending.

0) The 30-second rule

Pick your model by:

  • Quality (smartest answers)
  • Speed (snappy UX)
  • Cost (won’t burn credits)
  • Inputs (text only vs images/audio/PDFs)
  • Tool use (function calling / agents / structured JSON)

1) Quick picker: “What are you building?”

A) “My app chats with users” (general chatbot, support bot, tutor)

Start here (balanced):

  • OpenAI GPT-5 mini (fast + cost-efficient for well-defined tasks) (OpenAI Platform)
  • Anthropic Claude Sonnet 4.5 (Anthropic itself recommends it as the best balance of intelligence/speed/cost) (Claude)
  • Google Gemini Flash tier (designed for low latency / efficiency; see Gemini Flash/Flash-Lite lines in Gemini model docs) (Google AI for Developers)

Upgrade when you need “smarter”:

  • OpenAI GPT-5.2 / GPT-5.2 pro (positioned as best for coding + agentic tasks; “pro” for more precision) (OpenAI Platform)
  • Claude Opus 4.5 (premium intelligence tier) (Claude)
  • Gemini Pro tier (Google describes Pro as their advanced “thinking” model) (Google AI for Developers)

B) “I need cheap + fast text transforms” (summaries, rewrite, classify, moderation-ish labeling)

Use small/fast models:

  • OpenAI GPT-5 nano (OpenAI explicitly calls it fastest/cheapest and “great for summarization and classification”) (OpenAI Platform)
  • Claude Haiku 4.5 (Anthropic’s fastest tier) (Claude)
  • Gemini Flash-Lite (Google markets Flash-Lite as “fastest… optimized for cost-efficiency and high throughput”) (Google AI for Developers)

Best beginner move: start cheap, then only upgrade the model if quality is failing.

C) “My app writes/edits code” (code helper, debugging, refactors, generating files)

Strong picks:

  • OpenAI GPT-5.2 (explicitly positioned for coding + agentic tasks) (OpenAI Platform)
  • Claude Sonnet 4.5 (Anthropic highlights exceptional coding + agent performance) (Claude)
  • Gemini Pro (strong long-context reasoning + code/document analysis per Google’s model docs) (Google AI for Developers)

If you’re building a “coding agent” (it plans + edits multiple files), prioritize models that do tool use + long context well.

D) “My app uses tools / agents” (calls functions, hits APIs, multi-step tasks)

You want models that are reliable with tool calling, not just vibes.

Good starting points:

  • OpenAI GPT-5.2 (explicit tool calling + context management guidance) (OpenAI Platform)
  • Claude Sonnet 4.5 (explicitly positioned for complex agents) (Claude)
  • Gemini Flash-Lite / Pro (Gemini docs list function calling + code execution + file search capabilities on certain models) (Google AI for Developers)

E) “RAG / search over docs” (chat with PDFs, knowledge base bot)

RAG apps care about long context + retrieval quality + structured output.

Great for RAG-style apps:

  • Cohere Command R / R+ (Cohere recommends R+ for complex RAG and multi-step tool use) (Cohere Documentation)
  • Gemini Pro / Flash-Lite (very large input limits + built-in capabilities listed in Gemini docs) (Google AI for Developers)
  • Claude Sonnet 4.5 (strong long context; supports image input; model table includes context info) (Claude)

F) “My app needs vision” (analyze screenshots, images, UI bugs, receipts, diagrams)

Pick a model that officially supports image input.

  • OpenAI GPT-5.2 family includes multimodality/vision in its guidance (OpenAI Platform)
  • Claude models: “all current Claude models support text and image input” (Claude)
  • Gemini models: many accept text + images/video/audio/PDF as inputs (see model cards) (Google AI for Developers)

G) “My app needs audio / realtime voice”

If you want voice chat or real-time speech, pick a provider with dedicated audio/realtime models.

2) “I don’t want vendor lock-in” options (open models)

If you want to try open-source models without running GPUs yourself:

  • Groq hosts models like Llama and exposes an OpenAI-compatible chat completions endpoint (GroqCloud)
  • Together.ai offers many open models (chat/code/vision/audio) and advertises OpenAI-compatible APIs (Together AI)
  • Mistral provides its own API + model list docs if you want Mistral-hosted models (Mistral AI)

These are awesome for: fast prototypes, cost control, and experimenting with different model “personalities.”

3) Beginner “default picks” (if you don’t want to think)

  • Cheap + fast: GPT-5 nano / Claude Haiku 4.5 / Gemini Flash-Lite (OpenAI Platform)
  • Balanced (most apps): GPT-5 mini / Claude Sonnet 4.5 / Gemini Flash (OpenAI Platform)
  • High quality / hard problems: GPT-5.2 (or pro) / Claude Opus 4.5 / Gemini Pro (OpenAI Platform)

4) Replit-specific safety tip (please don’t skip)

  • Put API keys in Replit Secrets / env vars
  • Call the LLM from your backend, not directly from browser code
  • Add rate limiting + caching if your app gets traffic

Hope this helps some of you who aren’t familiar with API keys . I didn’t really see a section explaining API keys and LLMs . I could have went in more depth but just wanted something for people unfamiliar and new to vibe coding .

-404

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Secrets & API Keys 101 (Replit beginners): How not to leak your keys and get cooked

If you’re new to APIs on Replit, this is the #1 mistake that will ruin your day:

Putting API keys in your code or frontend.

An API key is basically your app’s credit card + identity. If it leaks, someone can:

  • run up your bill

  • get your account rate-limited/banned

  • steal access to paid tools

The golden rules (easy mode)

1) Never hardcode keys in your repl
Bad:

  • OPENAI_API_KEY = "sk-..." in a file

  • keys in GitHub commits

  • keys in screenshots

2) Never put keys in frontend code
If your key is in:

  • index.html

  • client-side JavaScript

  • a React app running in the browser
    …it can be viewed by anyone.

3) Store keys in Replit Secrets
Use Secrets / Environment Variables so keys aren’t in your codebase.

4) Call APIs from the backend
Correct flow:
Frontend → your backend route → external API → backend returns safe result
Your key stays server-side.

5) Add basic protection
Even for small projects:

  • rate limit endpoints

  • validate inputs

  • cache results (especially AI calls)

  • log usage

Quick mental check: “Am I leaking my key?”

If a stranger can open DevTools / view source and find your key → you leaked it.

Common beginner setups (what to do instead)

If you’re building a frontend app:
Make a backend route like /api/chat that uses the key internally.

If you’re building a Discord bot / automation:
Keys go in Secrets, bot runs server-side only.

If you’re using an LLM API:
Frontend sends prompt → backend calls LLM → backend returns response.

-404

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Would Replit’s “Run a security check” or “check for bugs” prompts catch such potential security leaks?