BE-3: LLM APIs & Free Tier Chatbot Models Research (Gemini, Grok, OpenAI Alternatives)

[tecnodeveloper]

Description:
Research available LLM APIs and free-tier chatbot models. Compare their capabilities, limits, pricing, rate limits, and suitability for building an agent-based system like Price WatchDog. Focus on choosing a model that can power reasoning, extraction, and decision-making.

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User Story

Given I need an AI brain for my scraping agent
When I choose an LLM API
Then I should know which model is best for cost, performance, and reliability

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Tasks

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LLM Basics

1. Understand What LLM APIs Are

   • What is an LLM (Large Language Model)
   • API-based inference vs local models
   • Token-based pricing concept

2. Understand Use Cases in Project

   • Data extraction reasoning
   • Product understanding
   • Price comparison decisions
   • User interaction chatbot

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Google Gemini Research

3. Study Google Gemini

   • Free tier availability
   • API usage limits
   • Multimodal capabilities

4. Gemini Strengths & Weaknesses

   • Fast responses
   • Good reasoning
   • Rate limits in free tier

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OpenAI Models Research

5. Study OpenAI APIs

   • GPT-3.5 / GPT-4 models
   • API pricing structure
   • Function calling ability

6. OpenAI Pros & Cons

   • Very strong reasoning
   • Paid usage after free credits
   • Stable ecosystem

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Grok Research

7. Study xAI Grok Model

   • Access via X platform
   • Free vs paid availability
   • Real-time knowledge capabilities

8. Grok Pros & Cons

   • Strong conversational ability
   • Limited API access
   • Less stable developer tooling

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Other Free Tier Models

9. Explore Other LLM Options

   • Claude (Anthropic)
   • Mistral AI models
   • Cohere API

10. Compare Free Access Limits

• Tokens per minute
• Daily request limits
• Context window size

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API Capability Comparison

11. Reasoning Ability

• OpenAI (high)
• Gemini (high)
• Grok (medium-high)

12. Speed Comparison

• Gemini (fast)
• OpenAI (medium-fast)
• Grok (variable)

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Cost Analysis

13. Free Tier vs Paid Tier

• Free usage limits
• Pay-per-token models
• Budget estimation for project

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Integration Ease

14. API Developer Experience

• REST API simplicity
• SDK availability
• Documentation quality

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Agent Suitability

15. Best Model for Agents

• Tool calling support
• Structured output ability
• JSON response formatting

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Use Case Mapping

16. Which Model for What Task

• Gemini → fast reasoning + free usage
• OpenAI → complex reasoning + structured output
• Grok → conversational assistant

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Rate Limits & Stability

17. Understand API Limits

• Requests per minute
• Token limits
• Throttling behavior

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Security & Reliability

18. API Safety Considerations

• Key protection
• Request logging
• Fallback model strategy

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Hybrid Strategy

19. Multi-LLM System Design

• Primary model (Gemini/OpenAI)
• Fallback model
• Cost optimization routing

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Real-World Usage

20. Agent Use Case Testing

• Product extraction prompt
• Price comparison reasoning
• Structured JSON output

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Acceptance Criteria

• Multiple LLM APIs researched
• Free tier limits understood
• Comparison completed
• Best model candidates identified
• Agent use-case mapping defined

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Testing Steps

1. Test Gemini API
2. Test OpenAI API
3. Compare response quality
4. Measure latency
5. Check free-tier limits

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Definition of Done

• LLM ecosystem fully researched
• Best model strategy defined
• Cost + performance tradeoffs understood