GPT-4 to GPT-4o migration savings calculator
If you are still calling the original GPT-4, you are almost certainly overpaying. GPT-4o delivers comparable quality at a small fraction of the input-token price, making the migration one of the highest-return cost moves available. This calculator turns your current usage into exact monthly and annual savings, and models a phased rollout so you can see the gain accrue as you ramp.
How it works
The tool prices your monthly token volume under both models. GPT-4 and GPT-4o each bill input and output separately:
cost = (input_tokens / 1e6) × input_price + (output_tokens / 1e6) × output_priceWith original GPT-4 input around $30 per million and GPT-4o around $2.50, the input side alone shrinks roughly ten-fold. The rollout slider blends the two: at X% migrated, that share of your tokens is priced at GPT-4o rates and the remainder stays on GPT-4, so the savings line tracks your actual rollout progress.
Tips and notes
Migrate low-risk, high-volume traffic first — that is where the savings are largest and the quality risk is smallest. Verify on your own evals before moving critical paths; GPT-4o matches GPT-4 on most tasks but occasionally differs in tone or formatting, which a prompt tweak usually fixes. Watch the output side: if your workload is generation-heavy, the output price still matters even though the headline win is on input. Re-run this calculator with real numbers from your OpenAI usage dashboard rather than estimates — the savings are usually larger than people expect, and the annual figure makes the business case obvious.
How to plan the migration in practice
Finding your token numbers
Your exact monthly token volumes are available in the OpenAI usage dashboard at platform.openai.com under Usage. The dashboard separates prompt (input) and completion (output) tokens, and you can filter by model and date range. Export the monthly totals and enter them here for an accurate calculation. Using rough estimates significantly understates the savings because input and output often split unevenly — many workloads are input-heavy (long system prompts, RAG context) with short completions, and that is precisely where the GPT-4o discount is largest.
Recommended phasing for a production migration
| Phase | Traffic migrated | Action |
|---|---|---|
| 1 — Pilot | 5–10% | Route a small share of non-critical requests; run evals; monitor error rates |
| 2 — Validate | 20–30% | Extend to more request types; check for tone/format regressions |
| 3 — Ramp | 50–70% | Move most traffic; keep a GPT-4 fallback for edge cases |
| 4 — Full | 100% | Retire GPT-4 endpoints; update monitoring |
The slider in this calculator models any point in this ramp, so you can see the monthly savings at each stage and build an internal business case for moving faster.
Prompt compatibility
GPT-4o generally follows system and user prompts written for GPT-4 without changes. The most common adjustment needed is with prompts that specified formatting in a particular way — GPT-4o may add or omit markdown formatting elements differently. A one-time pass through your prompt library to check for these differences is worth the effort and is typically an afternoon’s work for a moderate codebase.
Output token economics
The example above emphasises input savings because the price delta there is the most dramatic. But if your application generates long outputs — summaries, reports, code — the output side matters too. For a 50% completion-heavy workload, the output price reduction compounds the input savings significantly. Check the per-request costs the calculator shows for both sides, not just the total.