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OpenAI API Pricing Guide 2026: GPT-4o, o1, o3-mini

OpenAI API Pricing Guide 2026: GPT-4o, o1, o3-mini

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OpenAI API Pricing Overview

OpenAI uses a token-based pricing model where you pay separately for input tokens (your prompt) and output tokens (the model’s response). Pricing varies significantly between models, so choosing the right model for your use case can reduce costs by 10x or more.

All prices below are per 1 million tokens (1M tokens) as of early 2026. Prices change frequently, so always verify against OpenAI’s official pricing page.

Current Model Pricing

GPT-4o

GPT-4o is OpenAI’s flagship multimodal model, capable of processing text, images, and audio. It offers a strong balance of intelligence and speed.

  • Input: $2.50 per 1M tokens
  • Output: $10.00 per 1M tokens
  • Context window: 128K tokens

GPT-4o is the go-to model for most production applications that need high-quality responses. Its 128K context window is large enough for most use cases.

GPT-4o-mini

GPT-4o mini is a smaller, cheaper version of GPT-4o designed for high-volume, lower-complexity tasks.

  • Input: $0.15 per 1M tokens
  • Output: $0.60 per 1M tokens
  • Context window: 128K tokens

At roughly 16x cheaper than GPT-4o, this is ideal for classification, summarization, and simple question-answering tasks where top-tier reasoning is not required.

o1

OpenAI’s reasoning model that “thinks” before responding. It excels at math, coding, and complex multi-step problems.

  • Input: $15.00 per 1M tokens
  • Output: $60.00 per 1M tokens
  • Context window: 200K tokens

o1 is expensive but powerful for tasks that require careful reasoning. Use it selectively for high-value queries, not for simple chat.

o3-mini

A more affordable reasoning model that balances cost and capability.

  • Input: $1.10 per 1M tokens
  • Output: $4.40 per 1M tokens
  • Context window: 200K tokens

o3-mini brings reasoning capabilities to a more accessible price point. It is suitable for applications that benefit from chain-of-thought reasoning without the premium cost of o1.

Real-World Cost Examples

Customer Support Chatbot

A typical customer support interaction involves a 500-token system prompt, a 100-token user message, and a 300-token response.

Using GPT-4o-mini: (600 input tokens + 300 output tokens) per query. At 10,000 queries per day:

  • Input: 6M tokens x $0.15 = $0.90/day
  • Output: 3M tokens x $0.60 = $1.80/day
  • Total: $2.70/day or $81/month

Using GPT-4o for the same volume:

  • Input: 6M tokens x $2.50 = $15/day
  • Output: 3M tokens x $10 = $30/day
  • Total: $45/day or $1,350/month

The 16x price difference between GPT-4o-mini and GPT-4o is significant at scale.

Document Summarization

Summarizing a 10,000-word document (roughly 13,000 tokens input) with a 500-token summary output, processing 100 documents per day:

Using GPT-4o-mini:

  • Input: 1.3M tokens x $0.15 = $0.20/day
  • Output: 50K tokens x $0.60 = $0.03/day
  • Total: $0.23/day or $7/month

Code Review

Reviewing a 2,000-line code file (roughly 8,000 tokens) with a 2,000-token review output, 50 reviews per day:

Using o3-mini (for reasoning quality):

  • Input: 400K tokens x $1.10 = $0.44/day
  • Output: 100K tokens x $4.40 = $0.44/day
  • Total: $0.88/day or $26/month

Choosing the Right Model

Use GPT-4o-mini When:

  • Tasks are straightforward (classification, extraction, simple Q&A)
  • You need high throughput at low cost
  • Response quality does not need to be best-in-class

Use GPT-4o When:

  • You need high-quality, nuanced responses
  • Tasks involve complex instructions or creative writing
  • Accuracy is more important than cost

Use o3-mini When:

  • Tasks require multi-step reasoning
  • Math, logic, or code generation is involved
  • You need reasoning quality without o1’s premium price

Use o1 When:

  • Tasks require the deepest reasoning capabilities
  • You are solving novel problems, proofs, or complex analysis
  • Quality justifies the 6x cost premium over GPT-4o

Cost Optimization Tips

  1. Start with the cheapest model — Test GPT-4o-mini first. Only upgrade if quality is insufficient.
  2. Cache common prompts — If many requests share the same system prompt, use OpenAI’s prompt caching to reduce input costs by 50%.
  3. Shorten your prompts — Remove unnecessary instructions and examples. Every token costs money.
  4. Set max_tokens — Limit output length to prevent unexpectedly long (and expensive) responses.
  5. Use streaming — Stream responses to reduce perceived latency without changing cost.

Calculate Your Costs

Estimate your exact API costs with the tokencalc Pricing Calculator. Input your expected volume, select your model, and see projected daily, monthly, and yearly costs. Compare multiple models side-by-side to find the most cost-effective option for your workload.

For token counting, use the Token Counter to see exactly how many tokens your prompts and responses consume before running them through the API.