Pricing Comparison: OpenAI vs Gemini

This data is correct as of January 22nd, 2026.

As generative AI matures, the pricing models have fragmented. OpenAI, Google Gemini, and Anthropic Claude now each offer multiple model tiers, subscription plans, and API pricing structures. For developers integrating these models, understanding the cost implications is essential. For companies reselling AI capabilities to their own customers, the complexity compounds.

This guide compares current pricing across the three major providers and explains why this matters for your AI monetization strategy.

Understanding token-based pricing: the foundation

All three providers charge based on tokens which are chunks of text representing roughly 4 characters or 0.75 words. Pricing is split between input tokens (what you send) and output tokens (what the model generates), with output typically costing 3-5x more than input.

This asymmetry means that a customer interaction that generates a long response costs significantly more than one that receives a short answer. For AI products, this creates margins that traditional subscription billing wasn't designed to handle - and it's a lot more variable.

OpenAI and Gemini for consumers

API pricing (per 1M tokens)

For developers, the pricing is a bit different:

OpenAI API

Sometimes the batch API offers 50% discount, and prompt caching saves between 50 and 90% on repeated content.

Google Gemini API

The free tier available for all models (rate-limited). Batch API offers 50% discount.

Anthropic Claude API

The batch API offers 50% discount. Prompt caching saves up to 90%.

Why this is still complicated

The tables above show the base rates you can expect, but actual costs depend on a few more factors:

1. Model selection per request. Different tasks require different models. A simple classification might use Haiku ($0.25/M), while complex reasoning needs Opus ($5/M)—a 20x difference.

2. Context length. Pro-tier models charge 2x for requests exceeding 200K tokens. A document analysis workflow can hit this threshold easily.

3. Caching and batching. Repeated prompts can be cached for 90% savings. Non-urgent work can be batched for 50% off. But these optimizations require infrastructure.

4. The output variability. You control input tokens. You don't fully control output tokens. A model that "thinks longer" costs costs more and reasoning models like o1 use hidden "thinking tokens" that don't appear in the response but do appear on your bill.

Planning an AI product? Here's how to think about these prices

If you're building an AI product, this pricing complexity becomes your pricing complexity.

• The margin problem. Every user interaction has variable cost. A power user generating long responses costs 10x more than a casual user. Traditional per-seat pricing doesn't account for this.

• The model mix problem. Sophisticated AI products route different requests to different models. Your billing system needs to understand which model served which request and price accordingly.

• The credit translation problem. Many AI products abstract this complexity with "credits." But now you're maintaining a conversion layer: usage → credits → dollars. And when providers change pricing (which happens regularly), you're re-engineering that layer.

• The visibility problem. Finance needs to understand margin by customer, by feature, by time period. If your metering system lives separately from your billing system, this reconciliation happens in spreadsheets.

Further Reading

Understanding AI pricing is the first step. The harder question is how to monetize AI capabilities in your own product without the billing infrastructure becoming a bottleneck.

Contact us to learn more on how we can help you with architecting credits and move beyond seat-based pricing, or head to our blog for more insights.

Pricing data current as of January 2026. For the latest rates, refer to the official pricing pages: OpenAI, Google Gemini, Anthropic Claude.