Choosing the Right AI Model

Picking the wrong AI model is like trying to use a spreadsheet to manage a CRM — technically possible, deeply painful, and expensive. The major models differ significantly in capability, cost, speed, privacy posture, and context window size.

Choosing well means faster results, lower costs, and workflows that actually hold up at scale. Choosing poorly means paying for capability you don't need, or using a model too weak for your task.

The good news: the decision isn't hard once you have a framework.

Evaluate every model on four dimensions:

Capability: How good is it at your specific task? General benchmarks are noisy. The only test that matters is your task, your data, your prompt. Run a sample before committing.

Cost: Most models price by token (roughly, words in + words out). For production workloads, cost differences between models can be 10-50x. A task that costs $0.001 on Haiku costs $0.015 on Opus. At 100,000 runs per month, that's a $1,400 vs $14,000 monthly difference.

Latency: How fast does it respond? Matters enormously for interactive apps. For batch jobs run overnight, it's irrelevant. Know whether your use case is latency-sensitive before over-optimizing for speed.

Context window: How much text can you feed it at once? GPT-4o: 128K tokens. Claude 3.5 Sonnet: 200K. Gemini 1.5 Pro: 1M. For long document analysis or large codebases, context window is the deciding factor.

GPT-4o (OpenAI): The most widely used, best ecosystem integrations, strong at instruction following, reliable code generation. Good default choice if you're in the OpenAI ecosystem.

Claude 3.5 Sonnet (Anthropic): Excellent at long documents and nuanced writing. 200K context window. Exceptionally good at following complex instructions precisely. Strong choice for anything involving analysis of long texts.

Claude 3 Haiku (Anthropic): Fast and cheap — roughly 80% of Sonnet quality at 5-10% of the cost. Best choice for high-volume, lower-stakes tasks: classification, extraction, summarization at scale.

Gemini 1.5 Pro (Google): 1M token context window — the largest available. Best choice for tasks requiring processing of very long documents, entire codebases, or hours of transcripts. Competitive pricing.

Llama 3 (Meta, open source): Runs locally or on cheap inference providers. Zero privacy concerns — your data never leaves your infrastructure. Best choice for sensitive data or cost-critical workloads where you can invest in infra.

Mistral / Mixtral: Fast, cheap European models with strong multilingual support. Good default for EU workloads with data residency requirements.

Run through these questions in order:

1. Is the data sensitive? If yes → local model (Llama) or EU-hosted model (Mistral). Stop here.
2. Is cost the primary constraint? If yes → Claude Haiku or GPT-3.5. Test quality. If acceptable, ship it.
3. Is the document very long (>50 pages)? If yes → Gemini 1.5 Pro for its 1M context window.
4. Is it a writing or analysis task that requires nuance? → Claude 3.5 Sonnet.
5. Is it a code or structured output task? → GPT-4o or Claude 3.5 Sonnet. Both are strong; test with your actual cases.
6. Default: GPT-4o or Claude 3.5 Sonnet. They're the most capable general-purpose models at a reasonable price.

Never commit to a model for a production workflow without running your actual task against your actual data. The following testing protocol takes 2 hours and saves weeks:

1. Take 10-20 representative inputs from your real data.
2. Write a baseline prompt and run it against your top 2-3 model candidates.
3. Score the outputs on your criteria (accuracy, format, tone — whatever matters for your use case).
4. Record latency and cost per run.
5. Pick the model that hits your quality bar at the lowest cost.

This is the only evaluation that matters. Benchmarks are marketing.