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The current OpenAI lineup spans GPT-5.5, GPT-5.4, GPT-5, and GPT-5 Mini. The table below reads the current rates from the shared benchr model data so you can compare the tiers on one basis.
| Model ID | Input / 1M | Output / 1M | Cached Input / 1M | Batch Input / 1M |
|---|---|---|---|---|
| gpt-5.5 | $5.00 | $30.00 | $0.500 | $2.50 |
| gpt-5.4 | $2.50 | $15.00 | $0.250 | — |
| gpt-5 | $1.25 | $10.00 | — | — |
| gpt-5-mini | $0.250 | $2.00 | $0.025 | — |
Pricing Strategy: Optimizing OpenAI Costs
If your application relies heavily on OpenAI models, you can employ several tactics to cut your monthly bills:
- Use Batch API for async work: OpenAI lists a 50% discount for eligible Batch API input and output tokens. Jobs use a documented 24-hour completion window, so they suit work that can wait; handle failed, expired, or canceled batches and verify current eligibility.
- Enable Prompt Caching: Dynamic system prompts and repetitive contexts are cached automatically. GPT-5.5 and GPT-5 Mini feature a 50% and 90% discount respectively for cached input hits.
- Use Structured Outputs wisely: A strict JSON schema counts toward input tokens, and first-request schema processing can affect latency. Reuse stable schemas where appropriate, then measure latency and valid-output rate on your workload.
For cross-provider comparisons, read the global AI Model Pricing Comparison, or calculate custom usage via the Cost Calculator.
Where GPT-5.4 fits
The ladder gained a rung in March 2026. GPT-5.4 sits at exactly double GPT-5 and exactly half of GPT-5.5: $2.50/1M input, $15/1M output, cached input at $0.25. What the middle rate buys is context up to 1M tokens (GPT-5 stops at 400K), built-in computer use, and the finance tuning that launched ChatGPT for Excel. It carries the same 272K long-context surcharge structure as GPT-5.5, so the headline rate only holds below that line. The GPT-5.4 pricing breakdown has the full math.
Budgeting OpenAI workloads
OpenAI pricing decisions usually come down to routing. GPT-5 Mini is the volume tier, GPT-5 is the default general-purpose tier, GPT-5.4 is the context-and-computer-use middle, and GPT-5.5 is the expensive tier for work where higher capability measurably reduces failure. Treat those as routing lanes, not as interchangeable chat models.
Before standardizing on one model, separate calls by risk. Use the smaller tier for extraction, classification, and simple drafting; use the middle tier for normal user-facing answers; reserve the flagship tier for tasks where a wrong answer creates enough review cost or product risk to justify the premium.
What to measure after launch
After deployment, track model cost by feature rather than by account total. The important lines are average input tokens, average output tokens, retry rate, and fallback rate per feature. A chat feature, an extraction feature, and a coding feature can have completely different economics even when they use the same OpenAI model.
Review the routing rules monthly. If GPT-5 Mini starts passing the same checks as GPT-5 for a narrow workflow, move that workflow down. If GPT-5 produces expensive review failures in a high-risk area, move only that area up to GPT-5.5 instead of upgrading everything.
For teams already using OpenAI, the cheapest optimization is often prompt discipline rather than a model switch: shorter system messages, structured outputs that prevent verbose answers, and caching repeated instructions where the platform supports it. Clean prompts reduce cost and make evals more stable.
Finally, keep a simple monthly price audit. Provider pricing, batch discounts, and model aliases can change faster than product teams notice. A five-minute check against the official pricing page and your own usage dashboard prevents a quiet model change from becoming a surprise bill.