How to Compare AI Models

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.

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:

• 4K-8K: Short conversations, simple Q&A
• 32K-64K: Long documents, multi-turn conversations
• 128K: Books, codebases, long research papers
• 200K-1M+: Entire repositories, multiple books at once

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:

Two metrics matter for speed:

• Latency (time to first token): How fast the model starts responding. Critical for interactive chat.
• Throughput (tokens per second): How fast tokens stream after the first one. Matters for long outputs.

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.