AI Pricing Index

How Much Does AI Actually Cost?

Live pricing data across 20+ AI models and tools — updated monthly. No signup required.

Last updated: 2026-05 · Source: OpenRouter

Key Findings 2026-05

• Frontier AI costs $21.13 per million tokens — that's roughly $0.02 per 750-word blog post generated by GPT-4o or Claude Sonnet. For most small businesses processing under 10,000 tasks per month, AI API costs are under $50/month.
• Budget models deliver 80-90% of frontier quality at 28x lower cost. The Budget Index sits at $0.76/1M tokens — models like Gemini Flash, DeepSeek V3, and Llama 4 handle email drafting, customer replies, and document processing at near-zero marginal cost.
• The price gap between providers is widening. OpenAI's o-series reasoning models cost up to $262/1M tokens, while open-source alternatives from Meta and DeepSeek deliver strong results under $1/1M tokens. The "right" model depends entirely on the task.
• Most small businesses are overpaying. Consumer subscriptions ($20-200/month) include usage caps and features most users don't need. API access to the same models costs 5-20x less for typical small business workloads.

Data sourced from OpenRouter public API. Updated monthly. Cite as: AIscending AI Pricing Index, 2026-05. Questions? [email protected]

What Do These Numbers Mean?

The AI CPI tracks the average cost of using top-tier AI models (GPT-4o, Claude Sonnet, Gemini Pro, etc.) per million tokens — roughly 750,000 words of input/output combined. If you're building with AI APIs, this is your benchmark.

The Budget Index tracks the same metric for cost-efficient models (Gemini Flash, DeepSeek, Llama, Mistral Small). These deliver 80-90% of frontier quality at a fraction of the price — and they're what most small businesses should actually use.

How to Read This Data

Blended Price is what you'd actually pay per million tokens using a typical mix of 75% input and 25% output. Think of it as the "real-world cost" of using the model. Input tokens (what you send) are almost always cheaper than output tokens (what the model writes back), so the blend gives you a single number to compare apples to apples.

Per 1M Tokens — one million tokens is roughly 750,000 words, about 10 novels. That sounds like a lot, but most small business tasks use only 1,000 to 5,000 tokens per request. A quick customer email might use 500 tokens. Summarizing a 10-page contract might use 8,000. The per-million pricing is just how providers quote rates — divide by 1,000 to get the cost of a single typical request.

Context Length is how much text the model can "see" at once. A model with 128K context can process about 96,000 words in a single request — enough for a full book. Longer context means you can feed it bigger documents without splitting them up. Most everyday tasks need less than 8K tokens of context.

AI CPI is our composite index tracking how frontier AI pricing changes month to month — like the Consumer Price Index, but for AI. When the AI CPI drops, it means the same quality of AI output is getting cheaper. We track this so you can time your AI adoption to real market conditions, not hype cycles.

Current AI Model Pricing

Blended price per 1M tokens (75% input, 25% output). Lower is cheaper.

API Pricing — Per 1M Tokens

What developers and automation builders pay. All prices in USD via OpenRouter.

Tip: click any column header to sort.

Pricing by Category

Frontier models cost 10-50x more than efficiency models. For most small business use cases, the Budget Index models are the better value.

• 20x median gap. Frontier models cost a median of $6.00/1M tokens; open-source models cost $0.30. Same task, 20x the bill.
• Reasoning models hide a 905x range. From $0.29 at the low end to $262.50 at the top. Picking the wrong reasoning model can multiply your monthly AI spend.
• Efficiency tier handles 80% of small business tasks. If your use case is summarization, drafting, classification, or routing — there's no business reason to pay frontier prices.

Subscription Pricing — Monthly Plans

What consumers and business users pay for AI tools. No API needed.

Model Profiles

What each model is actually good at — and when to use it.

Frontier

Efficiency

Reasoning

Open Source

Pricing Trends Over Time

How AI model pricing has changed month over month. The trend is clear: costs are falling fast.

Why Do Some Models Cost 100x More?

Model size is the biggest factor. Frontier models like GPT-4o and Claude Opus run hundreds of billions of parameters — every token you send gets processed through all of them. More parameters means more compute per request.

Reasoning models (o1, o3, DeepSeek R1) are even more expensive because they run multiple internal passes before answering. You're paying for the model to "think" — sometimes generating 10x more hidden tokens than what you see in the response.

Open source competition is what drives the Budget Index down. Models like Llama and Mistral can be hosted by anyone, so providers compete on price. That's why a Llama 4 Scout query costs $0.14 per million tokens while an o1 query costs $262 — same unit of measurement, wildly different economics.

Methodology

• Source: OpenRouter /api/v1/models endpoint (free, public, 300+ models)
• Blended ratio: 75% input tokens / 25% output tokens — reflects typical usage patterns
• AI CPI: Average blended price across frontier + reasoning models (excludes free tiers)
• Budget Index: Average blended price across efficiency + open source models
• Frequency: Monthly snapshots on the 1st of each month
• Subscription pricing: Manually verified — check provider sites for current rates

Cite this page: "AI Pricing Index," AIscending.com, 2026-05. aiscending.com/ai-pricing-index/

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