Interactive LLM Price Calculator
Estimate your API spend by model price, token usage, and request volume.
Pricing (USD per 1M tokens)
Usage assumptions
Tip: most teams under-estimate output tokens. Track them separately.
Why an LLM Price Calculator Matters
LLM-powered products are easy to prototype and surprisingly expensive to operate at scale. A chatbot that seems cheap in testing can become a major line item once you add real traffic, longer prompts, and higher output lengths. This calculator helps you make cost visible before deployment so you can design for both quality and sustainability.
Whether you are building customer support automation, a coding assistant, internal search, or content workflows, the same rule applies: token usage multiplied by request volume drives your spend. Pricing transparency gives you better product decisions, cleaner experiments, and fewer budget surprises.
How to Use This Calculator
- Select a model preset or choose custom pricing.
- Enter input, output, and optional cached token rates in USD per 1 million tokens.
- Add your average token usage per request.
- Add expected requests per day and number of active days per month.
- Click Calculate Cost to see per-request, daily, monthly, and annual estimates.
Use conservative assumptions first. Then test your best-case and worst-case scenarios to create a realistic budget range.
Understanding the Three Token Buckets
1) Input tokens
These are the tokens you send to the model in each request: user message, system instructions, tool context, retrieved chunks, and metadata. In many apps, input tokens grow as prompts become more sophisticated.
2) Output tokens
These are tokens generated by the model. Output is often priced higher than input. If your product encourages long answers, reports, or code generation, output can dominate total cost even when request count is stable.
3) Cached tokens
Some providers offer lower rates for repeated prompt segments via context caching. If your app reuses stable prefixes (instructions, policy blocks, fixed schemas), cached pricing can significantly reduce costs.
The Core Cost Formula
A practical cost model is:
Total Cost = (Input Tokens × Input Rate) + (Output Tokens × Output Rate) + (Cached Tokens × Cached Rate)
Rates must be converted from “per 1M tokens” into “per token” for accurate math. This calculator does that conversion automatically and then scales the result by request volume.
Example Planning Scenarios
Scenario A: Customer Support Assistant
- 1,000–1,500 input tokens/request (history + knowledge snippets)
- 250–500 output tokens/request
- 5,000 requests/day
- 30 days/month
This is a common “looks cheap, scales fast” setup. A small per-request cost multiplied by high daily volume quickly becomes a meaningful monthly expense.
Scenario B: Internal Analyst Copilot
- 4,000+ input tokens/request (docs + tables + instructions)
- 700+ output tokens/request
- 500 requests/day
Lower traffic does not always mean lower spend. Large contexts and verbose outputs can produce similar monthly costs to higher-volume lightweight chat workloads.
How to Reduce LLM Spend Without Reducing Value
- Route by difficulty: use a smaller model for easy tasks and escalate only when needed.
- Cap response length: set sane max output tokens by feature.
- Trim context: retrieve fewer but higher-quality chunks.
- Use structured prompts: tighter prompts reduce unnecessary output verbosity.
- Cache stable prefixes: ideal for repeated instructions or templates.
- Measure per feature: cost should be attributed to workflows, not just one global model bucket.
Common Estimation Mistakes
- Ignoring output tokens or assuming they are always small.
- Using test prompts that are shorter than production prompts.
- Not accounting for retries, tool calls, and guardrail passes.
- Skipping seasonal traffic spikes and launch events.
- Forgetting that provider pricing can change over time.
Operational Best Practices
Create budget thresholds
Define soft and hard monthly limits. Trigger alerts before overages become painful.
Track quality next to cost
Cheaper is not always better. Evaluate cost-per-successful-task, not cost-per-request alone.
Review model mix monthly
As models and prices evolve, your best routing strategy can change. Re-benchmark regularly.
Final Thought
A good LLM product is not just smart—it is economically durable. Use this calculator early in design, revisit it with real telemetry, and keep your unit economics visible as your product grows.
Pricing presets are indicative examples and may not match real-time vendor rates. Always verify current pricing with your provider.