Choosing the Right AI Model for Your Product

With so many AI models available, choosing the right one for your product can feel overwhelming. Here’s how we make the decision at SquareCX.

The Model Landscape in 2025

The AI model market has exploded. Here are the main players:

OpenAI: GPT-5-Codex, GPT-4.1, GPT-4.0 Mini
Anthropic: Claude Sonnet 4.5, Claude Haiku 4.5
Google: Gemini 2.5 Pro, Gemini 1.5 Flash
Others: DeepSeek-R1, Qwen 3, Llama 3.3

Each has strengths and trade-offs. The key is matching the model to your use case.

Decision Framework

Here’s how we evaluate AI models for products:

1. Response Quality

Best for reasoning: Claude Sonnet 4.5, GPT-5-Codex
Best for speed: GPT-4.0 Mini, Claude Haiku 4.5, Gemini 1.5 Flash
Best for long context: Gemini 2.5 Pro (2M tokens), Claude Sonnet 4.5 (200K tokens)

2. Cost Considerations

Running an AI product at scale requires cost optimization:

Cost per 1M input tokens (approximate):
- GPT-5-Codex: $15
- Claude Sonnet 4.5: $15
- Gemini 2.5 Pro: $10
- GPT-4.0 Mini: $0.15
- Claude Haiku 4.5: $0.25
- Gemini 1.5 Flash: $0.075

Pro tip: Use expensive models for complex tasks, cheap models for simple tasks.

3. Latency Requirements

If your product needs real-time responses:

Sub-1 second: Claude Haiku 4.5, GPT-4.0 Mini, Gemini 1.5 Flash
1-3 seconds: GPT-5-Codex, Claude Sonnet 4.5
3+ seconds acceptable: Gemini 2.5 Pro (for long context tasks)

4. Specialized Capabilities

Different models excel at different tasks:

Code generation: GPT-5-Codex, Claude Sonnet 4.5
Creative writing: Claude Sonnet 4.5, GPT-4.1
Multilingual: Gemini 2.5 Pro, GPT-4.1
JSON mode: GPT-4.1, GPT-4.0 Mini
Function calling: GPT-4.1, Claude Sonnet 4.5
Vision: GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Pro

Real-World Examples

Here’s what we use in our products:

Content Generation Tool

User-facing generation: Claude Sonnet 4.5 (best quality)
Autocomplete suggestions: GPT-4.0 Mini (fast + cheap)
Content analysis: Gemini 1.5 Flash (cheap for large docs)

Customer Support Bot

Initial classification: Gemini 1.5 Flash (sub-second, cheap)
Complex responses: Claude Sonnet 4.5 (nuanced understanding)
Knowledge base search: Custom embeddings + Claude Haiku 4.5

Code Assistant

Code completion: GPT-5-Codex (specialized for code)
Bug explanations: Claude Sonnet 4.5 (excellent reasoning)
Quick fixes: GPT-4.0 Mini (fast iterations)

The Hybrid Approach

Don’t limit yourself to one model. Use a routing system:

function routeToModel(task) {
  if (task.type === 'simple' && task.budget === 'low') {
    return 'gpt-4o-mini';
  }
  if (task.type === 'code' && task.quality === 'high') {
    return 'gpt-5-codex';
  }
  if (task.contextLength > 100000) {
    return 'gemini-2.5-pro';
  }
  // Default to Claude for balanced quality/cost
  return 'claude-sonnet-4.5';
}

This approach can reduce costs by 60% while maintaining quality where it matters.

Model Reliability Considerations

Not all models are equally reliable:

Most consistent: Claude Sonnet 4.5, GPT-4.1
Occasional hallucinations: Gemini models
Rate limiting concerns: All providers during peak hours

Always implement fallback logic and input validation.

Our Recommendation

For most AI products, we recommend:

Start with Claude Sonnet 4.5 - Best all-around quality
Add GPT-4.0 Mini for scale - Use for simple tasks to save costs
Evaluate Gemini 2.5 Pro - If you need massive context windows
Test constantly - Models change, benchmarks aren’t everything

Cost Optimization Strategies

Real tactics that save money:

Cache system prompts - Reduce repeated context
Streaming responses - Better UX, no extra cost
Smart routing - Easy tasks → cheap models
Batch processing - Where real-time isn’t critical
Rate limiting - Prevent abuse from running up bills

What’s Next?

Building AI products requires more than just picking a model. You need the right architecture, error handling, monitoring, and optimization.

If you’re building an AI product and want help with the technical decisions—let’s talk.