Choosing the Right AI Model
A practical decision framework for matching models to tasks — based on cost, capability, and speed.
The capability–cost–speed triangle
Every model sits in a triangle of three constraints: capability (how well it handles complex tasks), cost ($ per million tokens), and speed (tokens per second). You can rarely optimize all three simultaneously. Frontier models (Claude Opus, GPT-4o, Gemini 1.5 Pro) are most capable but expensive and sometimes slower. Mid-tier models (Claude Sonnet, GPT-4.1 mini, Gemini 2.0 Flash) offer the best balance for most production tasks. Small models (Claude Haiku, GPT-4o-mini) are cheapest and fastest — ideal for high-volume, simple tasks.
Task-to-model matching
Complex reasoning, long documents (>100K tokens), coding, multi-step agents → Frontier tier. General chat, summarization, classification, standard coding → Mid-tier. Short text classification, extraction, simple Q&A, high-volume batch jobs → Small tier. For image understanding: Claude Sonnet, GPT-4o, or Gemini 1.5 Pro. For real-time streaming chat: Gemini 2.0 Flash or Claude Haiku. For strict privacy (no cloud): Llama 3.3 70B or Mistral Nemo, hosted locally.
Context window: how much text can the model see?
Context window is how many tokens the model can process in one call — including your prompt, retrieved documents, conversation history, and the response. Claude models offer up to 200K tokens; Gemini 1.5 Pro handles 1M. For document analysis, a longer context window is directly valuable. For chat, 32K is plenty. Beware: long contexts cost more and can degrade quality (models struggle with information in the middle of very long contexts).
Benchmark scores: what to trust
MMLU (general knowledge), HumanEval/SWE-bench (coding), GPQA (PhD-level reasoning), and MATH are commonly cited. Treat benchmarks as rough signals, not ground truth — they measure specific capabilities under specific conditions. A model that scores 5% higher on MMLU may not produce measurably better outputs for your marketing copy use case. The only benchmark that matters for you is testing on your actual task with your actual prompts. Run 20–50 representative examples and score the outputs manually.
Cost estimation in practice
Cost = (input tokens + output tokens) × price per million tokens. A typical chat message is 50–200 input tokens; a response is 100–500 output tokens. For a mid-tier model at $1/M input + $3/M output, that's roughly $0.001 per conversation turn — essentially free at low volume. It scales: 100,000 turns/day × $0.001 = $100/day. For batch document processing (long inputs, short outputs), input cost dominates — use a model with low input pricing.