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AI API Cost Guide 2026: Claude vs GPT-5.4 vs Gemini — Real Per-Token Pricing

Updated April 2026  ·  9 min read  ·  By Alex Doyle

Here's a number worth sitting with: the cheapest production-grade AI model right now costs $0.10 per million input tokens. The most expensive costs $5.00. Same task, same prompt, wildly different bill. If you're running 100,000 API calls a day and picked the wrong model, you're potentially burning $350,000 a year more than necessary — not because the expensive model is better for your use case, just because you never ran the comparison. This guide gives you the actual numbers and helps you stop overpaying.

How AI API Pricing Works

Every major AI provider charges per token — a chunk of approximately 3–4 characters of text. Your payment depends on two things: how many tokens you send in (your prompt, system instructions, conversation history) and how many tokens the model sends back (its response).

Output tokens are consistently more expensive than input tokens — typically 3–5x — because generating text is computationally harder than processing it. This means response length dramatically affects your bill.

The basic formula:

Cost = (input_tokens / 1,000,000 × input_price) + (output_tokens / 1,000,000 × output_price)

Current Pricing: April 2026

Prices verified April 2026. AI pricing changes frequently — always verify current rates at anthropic.com, openai.com, and ai.google.dev.

Real-World Monthly Cost Examples

Abstract per-token prices don't mean much without context. Here are three realistic use cases:

Customer Support Chatbot (1,000 req/day, 500 input + 300 output tokens)

Document Analysis Pipeline (100 req/day, 5,000 input + 1,000 output tokens)

Key Cost Levers: How to Cut Your AI Bill

1. Prompt Caching (50–90% off repeated context)

All three major providers offer prompt caching — if the same system prompt, document context, or conversation history is reused across requests, cached tokens are charged at a fraction of the standard rate. Anthropic charges 10% of standard input price for cache hits. OpenAI and Google offer similar discounts. For applications with consistent system prompts, this alone can cut input costs by 50–80%.

2. Batch Processing (50% off)

All providers offer a Batch API that processes requests asynchronously (typically within 24 hours) at 50% off standard rates. For non-real-time workloads — document processing, data enrichment, offline evaluations — batch mode is a no-brainer.

3. Model Routing

Not every request needs your most capable model. Route simple classification, extraction, or FAQ responses to a budget model (Gemini 2.5 Flash-Lite, GPT-5.4 nano) and reserve flagship models for complex reasoning, nuanced writing, or multi-step tasks. This "model cascade" approach can reduce costs by 60–80% without meaningful quality loss on simpler tasks.

4. Output Length Control

Since output tokens are 3–5x more expensive, keeping responses concise is valuable. Setting max_tokens limits, using structured output formats (JSON), and being specific in your prompts about desired response length all reduce costs.

Choosing the Right Model for Your Use Case

• Simple classification, routing, summarisation: Gemini 2.5 Flash-Lite or GPT-5.4 nano — cheapest reliable options at $0.10–0.20/M input
• Chatbots, content generation, code assistance: GPT-5.4 mini, Gemini 3 Flash, or Claude Haiku — strong quality at $0.50–1/M input
• Complex reasoning, long documents, nuanced tasks: Claude Sonnet 4.6, GPT-5.4, or Gemini 3.1 Pro — $2–3/M input range
• Hardest tasks, highest quality bar: Claude Opus 4.7 — most capable but 5x the cost of Sonnet
• Long-context document work (over 200k tokens): Claude Opus 4.7, GPT-4.1, or Gemini 3.1 Pro — all support 1M context window

Frequently Asked Questions