Before you commit to an AI model for production, you need a realistic monthly cost estimate based on your actual usage, not a generic pricing page. This calculator compares nine models across Claude, OpenAI, and Gemini using your request volume and token counts.
Enter your expected monthly API usage to compare costs across Claude, OpenAI, and Gemini models. All calculations run in your browser. Nothing is sent to a server.
| Model | Input cost | Output cost | Total/month | Cost per 1,000 requests |
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Pricing verified June 2026. Rates change frequently. Confirm with the provider's official pricing page before building a cost model for production.
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Pricing is verified as of June 2026. Provider rates change frequently, so confirm current pricing on each provider's official page before building a production cost model.
This calculator applies a 50% discount when batch mode is enabled, matching the standard batch pricing offered by major providers. Actual eligibility depends on your use case.
Cached input tokens are discounted: 90% off for Claude and Gemini, 50% off for OpenAI. Output tokens are never cached and are charged at full rate.
Batch pricing varies by model tier. Claude and Gemini typically offer the largest batch discounts on input tokens. Use this calculator with batch mode enabled to compare your specific volume.
No. Match the model to the task, classification and extraction can use smaller models; complex reasoning needs larger ones. A cheap model that requires rework costs more than a capable one used sparingly.
Operations should own outcomes and daily use; IT or a technical partner owns infrastructure, API keys, and uptime. The split fails when no one owns prompt tuning and accuracy reviews; assign that to a named business owner.
Scoped integrations often start around $5,000, $15,000 for a focused use case. Full production systems with monitoring, fallbacks, and admin tools typically run $15,000, $50,000 depending on data complexity and integrations.
Teams deploy without guardrails: no human review queue, no logging, no fallback when the API is down. Build those three before launch, not after the first incident.
Usually not. A developer who understands your stack plus a business owner who reviews outputs weekly is enough for most systems. Specialist help matters when you add RAG, fine-tuning, or compliance-heavy workflows.
Define one metric tied to the business problem: time saved, error rate, response time, or cost per transaction. Review it weekly for the first month, then monthly. If the metric does not move, the design needs adjustment, not more features.
We build custom AI systems. Projects start at $5,000.
