With 328+ AI models from 51 providers, choosing the right one can feel overwhelming. This guide breaks down the key factors to consider when comparing models for your specific use case.
Our composite quality score (0-100) combines multiple signals into a single comparable number. It's calculated from:
A score above 80 indicates a top-tier model. 60-80 is solid mid-range. Below 60 usually means older models or models with limited capabilities. Use the score as a starting point, then dig into the factors that matter most for your use case.
AI model pricing is based on tokens (roughly 4 characters per token). Costs are quoted per million tokens, with separate rates for input and output.
| Tier | Output $/1M | Typical Models |
|---|---|---|
| Free | $0 | Open-source on free tiers |
| Budget | <$1 | DeepSeek, small Llama, Flash |
| Mid-Range | $1-$15 | GPT-4o Mini, Haiku, Mistral |
| Premium | $15+ | GPT-4o, Claude Opus, o1 |
Key insight: Output tokens cost 2-5x more than input tokens. For chatbots that generate long responses, output cost dominates. For summarization (long input, short output), input cost matters more.
The context window is how much text a model can process in a single request (input + output combined). Measured in tokens:
Bigger isn't always better - most tasks fit in 32K tokens. Larger context windows cost more per request and may have slower response times. Choose based on your actual data size, not the biggest number available.
Modern AI models vary widely in what they can do beyond text generation:
Accept images as input - useful for image analysis, OCR, diagram understanding.
Invoke external tools and APIs - essential for AI agents and automation.
Chain-of-thought thinking for math, logic, and complex multi-step problems.
Guaranteed structured output - critical for production API integrations.
Real-time internet access for current information and source citations.
Token-by-token output - essential for responsive chat interfaces.
Two metrics matter for speed:
Reasoning models (o1, DeepSeek R1) trade speed for accuracy - they're slower but more correct on hard problems. For real-time chat, prioritize latency. For batch processing, throughput matters more.
| Use Case | Priority |
|---|---|
| Chatbot | Speed + Streaming |
| Code Generation | Quality + Tools |
| Content Writing | Output + Context |
| Data Extraction | JSON + Accuracy |
| Research | Web + Reasoning |
| Image Analysis | Vision + Quality |
| Batch Processing | Cost + Throughput |
| AI Agents | Tools + Reasoning |
Use our tools to find the perfect model for your needs.
Consider four factors: (1) performance on your specific task, (2) API pricing and budget, (3) required features (vision, streaming, function calling), and (4) deployment needs (cloud API vs self-hosted). Our comparison tool lets you evaluate models side-by-side.
Benchmark scores show capability, but real-world performance matters most. Key metrics include: accuracy on your task, latency (time to first token), cost per request, context window size, and output quality consistency.
Many production applications use multiple models - a fast cheap model for simple tasks and a powerful expensive model for complex ones. This routing pattern optimizes both cost and quality.