GPT-4 vs Claude Cost: How to Decide
A decision framework for choosing between GPT-4 and Claude based on real workflow token usage.
The best model is rarely the one with the cheapest per-token headline rate—it’s the one that finishes the job with fewer billed tokens.
Cost breakdown that actually matters
Compare: (1) tokens per call and (2) calls per user action. If one model needs fewer refinement passes, it can be cheaper overall even with higher rates.
A realistic example
For a drafting workflow: Model A drafts in 1 call, Model B needs 2 refinement calls. Multiply tokens by call count to see the difference.
Optimization idea (model routing)
Route tasks by complexity: cheap models for extraction/classification, premium models only for the final draft.
Guardrails
Cap retries and enforce budgets per agent so cost spikes don’t escape.
Related reading
A clear way to estimate Claude costs, including input vs output tokens and workflow call volume.
How to estimate DeepSeek costs and compare value across tasks, especially when you optimize prompts and reduce retries.
Understand Kimi pricing with input/output tokens and workflow call volume so you can control spend.
Understand OpenAI cost drivers and how to estimate cost per token for input and output.