OpenAI pricing: API cost per model, user, and month

OpenAI lists 43 priced models in the ByteCosts index, with GPT-5 Nano at about $0.155 per million tokens on ByteCosts' 70% input / 30% output blend. For most AI apps the bill is driven by output tokens, retry rate, and prompt-cache hit rate far more than the headline input price, so OpenAI is cost-effective when your workload is input-heavy (RAG, classification) or can cache a large shared prefix. Compare OpenAI against alternatives on a real workload - seats, requests, and token mix - before committing to a model or a subscription, because a cheaper per-token price can still lose once power users and long outputs are priced in.

OpenAI pricing at a glance

• Cheapest tracked chat model: GPT-5 Nano at $0.155 per 1M tokens (blended)
• Flagship: GPT-5.5 at $12.50 per 1M tokens (blended)
• Last recorded price event: gpt-5.5 cache read on Jun 1, 2026
• 43 priced models in the index, updated July 9, 2026

OpenAI model prices

Per-million-token list prices for current OpenAI models, from the ByteCosts pricing index. Output tokens usually cost several times more than input.

OpenAI subscription plans

Flat-rate OpenAI plans billed per month, separate from the per-token API pricing above. List prices; annual or quarterly billing can lower the effective rate. Verify the current price with OpenAI before subscribing.

Recent OpenAI price events

Source-backed price events recorded for OpenAI in the ByteCosts ledger, newest first. Every event below is a first-time listing - the ledger started tracking that rate; the model itself may be older.

When OpenAI is cost-effective

Token price alone does not decide cost. The variables that actually move an AI bill are: how many output tokens each call emits (output is the expensive side), how often calls retry, what fraction of the input prefix can be served from prompt cache, and how heavily your top 1% of users use the product.

OpenAI tends to win when your workload is input-heavy with short outputs, when you can cache a large shared system prompt or document context, or when a smaller OpenAI model clears your quality bar. It tends to lose when outputs are long and uncached, where a cheaper-per-output model compounds the saving across millions of calls.

Hidden costs to watch with OpenAI

List pricing rarely equals your invoice. Budget for these before you commit:

• Output-token blow-up: reasoning and verbose responses multiply the most expensive token class.
• Retries and tool loops: a single failed agent step can re-send the whole context.
• Cache misses: prompt caching only saves money above a break-even reuse count; cold prefixes pay the write premium.
• Power users: a small share of heavy users can dominate spend and erase a plan's margin.
• Egress and gateway fees: LLM gateways and observability add a percentage on top of raw model cost.

Limitations before production billing decisions

Treat ByteCosts calculations as planning estimates, not final billing totals. Real invoices can differ because token mix, retry rate, cache hit rate, rate limits, taxes, gateway fees, regional pricing, and negotiated discounts change the effective cost.

Verify the provider source before production billing decisions, then compare the estimate with your own logs or invoice once production traffic is live.

Lower-blend alternatives to OpenAI

On ByteCosts' 70% input / 30% output chat blend, these providers screen lower than OpenAI. Treat this as a shortlist, not a decision - check the selected model against your real token mix:

• Mistral - $0.040 blended via Ministral 3B (latest), 28 priced models.
• Cohere - $0.071 blended via Command R7B, 8 priced models.
• Alibaba Qwen - $0.095 blended via Qwen Turbo, 43 priced models.

Frequently asked questions

OpenAI pricing. ByteCosts. Updated July 9, 2026. https://bytecosts.com/pricing/openai/

Sources

• OpenAI official pricing
• OpenAI website

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