OpenAI API Pricing Explained (2026)

By Gia Gray · Updated July 2026 · 8 min read

OpenAI's lineup has turned over: the current generation is GPT-5.5, GPT-5.4, and GPT-5.4 mini, and the GPT-4 / o-series models are now legacy. I still see teams accidentally running deprecated models, paying for caching they never enabled, and reaching for the top-tier model on tasks a mini handles identically at a fraction of the cost. Here's what I actually use to pick a model and estimate what it'll cost — with real numbers.

OpenAI's Current Model Lineup and Pricing

Source: openai.com/api/pricing, July 2026. GPT-4o and GPT-4o mini are still callable but legacy; the o-series and GPT-4-class models have been folded into the GPT-5 line. Always verify before production use.

GPT-5.4: The Workhorse for Most Production Use Cases

GPT-5.4 is the model most teams should default to — fast, capable, multimodal, with a 400K context window, at $2.50/$15.00 per million tokens. For most text-based production workloads it's the sensible high-quality default.

Output pricing ($15/M) is where cost accumulates. If your app generates long responses — document drafting, detailed code explanations, multi-step reasoning — it adds up fast. A workload generating 500 tokens per response at 50,000 requests/month is already $375 in output alone.

GPT-5.4 is worth paying for when response quality directly affects the user experience, when you're doing complex reasoning or instruction-following, or when multimodal input is part of the pipeline.

GPT-5.4 mini: The Right Default for High-Volume Work

GPT-5.4 mini at $0.75/$4.50 per million tokens is one of the best deals in the OpenAI lineup — roughly 3–7× cheaper than GPT-5.4 and strong at classification, extraction, summarization, basic Q&A, translation, and simple code completion.

The common mistake is treating mini as "GPT-5.4 but worse." It's better framed as a different category — good enough for the large majority of tasks, drastically cheaper for the ones where quality differences don't reach users. Teams that route aggressively to mini often cut their bill 50–70% with no meaningful user-facing impact.

GPT-5.5 and Reasoning / Thinking Tokens

GPT-5.5 ($5/$30) is the frontier tier for the hardest coding, math, and multi-step reasoning. Like the whole current generation, it leans on thinking tokens — internal reasoning generated before the visible answer, billed at the output rate. On hard problems those can dominate the cost of a single request.

That shifts the metric that matters from cost-per-token to cost-per-correct-answer: a pricier model that gets it right the first time can be cheaper end-to-end than a cheap one you re-run three times. Reserve GPT-5.5 for tasks where the quality difference is real, and budget for thinking tokens explicitly on reasoning-heavy workloads.

Prompt Caching: ~90% Off Repeated Input

OpenAI caches repeated prompt prefixes (a long system prompt or document sent across many requests) and now charges cache hits at roughly 10% of the normal input rate — a ~90% discount, in line with Anthropic and Google.

This matters a lot for large, consistent system prompts. A 2,000-token system prompt across 100,000 requests/month is 200M input tokens. On GPT-5.4 that's $500 uncached vs roughly $50 on cache hits. Caching kicks in automatically for prompts sharing a common prefix of at least 1,024 tokens — just structure prompts so the stable parts come first.

Batch API: 50% Off Non-Realtime Requests

OpenAI's Batch API processes requests asynchronously (within 24 hours) at half price. For workloads that don't need immediate responses — bulk document processing, overnight analysis, bulk content generation — it halves your cost with no quality tradeoff.

Batch pricing on GPT-5.4: $1.25 input / $7.50 output per million tokens. On GPT-5.4 mini: about $0.375 / $2.25. Low rates for capable models.

What Model Should You Use?