LLM Cost Estimator
Estimate monthly costs for LLM-powered features. Compare pricing across GPT-4o, Claude, and open-source models to find the best fit.
Usage Parameters
GPT-4o Cost Estimate
OpenAIModel Comparison
| Model | Input / 1M | Output / 1M | Monthly | Per Query | Per User | Annual |
|---|---|---|---|---|---|---|
GPT-4o OpenAI | $2.50 | $10.00 | $60.00 | $0.0040 | $0.06 | $720.00 |
GPT-4o-miniCheapest OpenAI | $0.15 | $0.60 | $3.60 | $0.0002 | $0.0036 | $43.20 |
Claude Sonnet Anthropic | $3.00 | $15.00 | $85.50 | $0.0057 | $0.09 | $1.0K |
Claude Haiku Anthropic | $0.25 | $1.25 | $7.13 | $0.0005 | $0.0071 | $85.50 |
Llama 3.1 70B (hosted) Self-hosted / Replicate | $0.50 | $0.75 | $6.38 | $0.0004 | $0.0064 | $76.50 |
Monthly Cost Comparison
What is an LLM Cost Estimator?
An LLM cost estimator helps product teams forecast the monthly API spend of adding AI features to their product. Pricing varies widely across providers. GPT-4o, Claude, Gemini, and open-source models like Llama each charge different rates per input and output token. This calculator lets you plug in your expected request volume and token counts to get a side-by-side cost comparison before you commit to a provider. For a deeper look at choosing between models, see our LLM evaluation framework.
How to Use This Calculator
- Enter your usage. Estimate how many requests per day your feature will handle and the average input/output token length per request.
- Select models to compare. Pick the LLM providers you are evaluating. The calculator includes current pricing for major commercial and open-source models.
- Review the breakdown. See monthly cost estimates per model, including input vs. output token costs.
- Factor in trade-offs. The cheapest model is not always the best. Weigh cost against latency, accuracy, and context window size. Our LLM vs traditional ML comparison covers when a simpler approach might save you money.
FAQ
How accurate are these cost estimates?
The estimates use published per-token pricing from each provider. Actual costs can vary based on prompt engineering efficiency, caching, batching discounts, and rate tier. Treat these numbers as a planning baseline, then validate with a small production pilot.
Should I use a commercial model or self-host an open-source one?
Commercial APIs (OpenAI, Anthropic, Google) are easiest to start with. Self-hosting Llama or Mistral can cut per-token costs at scale, but adds infrastructure overhead. Our AI build vs. buy framework walks through the decision criteria.
How do I reduce LLM costs without switching models?
Shorter prompts, semantic caching, and batching requests are the three fastest wins. Also consider using a cheaper model for simple tasks and reserving the expensive model for complex reasoning. Check the AI unit economics framework for a structured approach to cost optimization.
Building an AI business case? Forge generates AI product PRDs with cost projections built in.