A field-tested decision framework for choosing between Anthropic Claude and OpenAI GPT, focused on what actually drives the invoice — not on benchmark scores.
You have large stable contexts (long system prompts, big tool definitions, persistent context windows) — prompt caching economics tilt strongly in Claude's favour
You need extended reasoning with explicit token-budget controls
You are running on AWS or GCP and want first-party Bedrock / Vertex integration
You value conservative tool-use behaviour in agentic systems
You have a wide mix of small tasks (classification, extraction, short generation) where the tier ladder (4o, 4o mini, future tiers) lets you route aggressively by complexity
You are running on Azure and want first-party Azure OpenAI integration
You need very-low-latency mid-tier inference for real-time UX
Your workload is heavy on multimodal (image input, audio) — GPT-4o has the broader modality footprint
Step 1 — Inventory your actual workload. Group calls by purpose: classification, extraction, generation, reasoning, agentic tool-use. The right answer is rarely "all on one provider".
Step 2 — For each group, run a 50-prompt evaluation set on both providers at the equivalent capability tier. Measure quality, latency p99, and per-call cost.
Step 3 — Project to 100% volume. Apply prompt caching where applicable. Multi-provider routing usually wins over single-provider lock-in above ~$10k/month spend.
Step 4 — Decide on a defensible primary + a defensible failover. Single-provider dependencies become liabilities at scale.
Step 5 — Re-benchmark quarterly. New model releases regularly shift the price/quality frontier; the optimal routing six months ago is rarely optimal now.
In most production audits we run, the question is not "which provider is cheaper" — it is "which workloads belong on which provider, with which prompt structure". Teams that pick a single provider and route everything through it almost always overspend. Teams that route classification and extraction through cheap mid-tier models on either provider, reserve frontier models for genuinely hard reasoning, and structure their prompts for cache reuse, almost always under-spend. Provider choice matters less than routing discipline.