LLM API Pricing Calculator

Compare API costs across every major LLM model. Adjust your query volume and token usage to see real-time monthly and annual cost projections, find the cheapest model for your workload, and share your analysis with your team.

1001,000,000
5010,000
105,000

Monthly Cost

$120.00

$45.00 input + $75.00 output

Cost per Query

$0.012

$12.00 per 1K queries

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Annual Cost

$1.4K

120.0K queries/year

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Monthly Cost by Model

Your Cost per Query vs. All-Model AverageAbove Average
Avg: $0.009
Your value: $0.012
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Recommended Actions

Top Performer

Monthly cost of $120.00 is very efficient — you are well-optimized.

🚀

Consider upgrading to a more capable model — at this volume, the quality difference may justify the cost.

đŸ“Ļ

Explore fine-tuning — at low volumes, a fine-tuned smaller model can deliver flagship quality at budget pricing.

🌐

Add more AI features to your product. At this cost level, AI is a high-ROI investment.

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Risk Radar

What happens to your monthly cost (inverted) if each variable drops by 15%?

âš ī¸ Monthly Queries is your most sensitive variable. A 15% decrease would change monthly cost (inverted) by $18.00

Understanding LLM API Pricing

Large language model APIs have become the backbone of modern AI applications, from chatbots and content generation to code assistants and data analysis. But pricing varies dramatically across providers and models. A single API call can cost anywhere from $0.00001 to $0.10+ depending on the model, token count, and whether you are processing input or generating output. Understanding these costs is essential for budgeting, choosing the right model, and building profitable AI-powered products.

How LLM Token Pricing Works

LLM providers charge based on tokens — sub-word units that represent text. For English, one token is roughly 0.75 words or about 4 characters. Pricing is split into input tokens (your prompt, system instructions, and context) and output tokens (the model's generated response). Output tokens are always more expensive because they require sequential computation — each new token depends on all previous tokens. For most models, output pricing is 3-5x the input price, which means controlling response length is one of the most powerful cost levers.

Comparing Model Tiers

The market has settled into clear tiers. Flagship models like GPT-4o, Claude Sonnet 4, and Gemini 2.5 Pro offer the best quality for complex tasks at moderate pricing. Budget models like GPT-4.1-mini, Gemini 2.5 Flash, and DeepSeek V3 deliver 80-90% of flagship quality at 5-20x lower cost. For teams building AI-powered marketing tools, Semrush provides marketing data APIs that pair well with LLM applications for competitive intelligence.

Strategies to Reduce LLM Costs

The most impactful cost reduction strategies target the largest line items: prompt engineering (shorter, more efficient prompts), model routing (using cheap models for simple tasks), response caching (avoiding duplicate generations), and output length limits (setting max_tokens). At scale, batch APIs can provide 50% discounts for non-time-sensitive workloads. To track how your AI costs translate into marketing ROI, try Semrush's analytics to measure the business impact of your AI-powered workflows.

Frequently Asked Questions

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