Quick takeaways
- Output tokens usually cost more than input tokens because generation is more compute-intensive.
- Small changes in daily call volume or response length multiply quickly across a month.
- Use this calculator to compare providers, then verify current pricing on official sites before committing.
Understanding LLM cost drivers
Umar Jamil covers evaluation and cost considerations when running LLM workloads.
Estimate your monthly spend
How the calculator works
The calculator multiplies your daily call volume by days per month, then applies each provider's approximate input and output price per million tokens. Prices are rounded and based on standard API tiers at the time of writing. They do not include batch discounts, caching, or negotiated enterprise rates.
For a detailed price table, see AI model API pricing compared.
Without AI vs. with AI
| Task | Without AI | With AI |
|---|---|---|
| Estimating spend | Engineers guess monthly cost from headline prices and are surprised by the bill. | The calculator turns calls per day, input tokens, output tokens, and days per month into a modeled spend. |
| Comparing providers | Teams pick a provider based on brand without modeling their actual workload. | Side-by-side cost results show which model is cheapest for the specific input/output mix. |
| Spotting cost drivers | Output length is treated as an afterthought. | The calculator makes clear that output tokens usually cost more and multiply quickly with volume. |
| Planning capacity | Capacity decisions are made without linking usage to dollars. | Modeled spend helps teams decide when caching, smaller models, or batching will pay off. |
| Negotiating rates | Teams enter enterprise discussions with no data on expected usage. | A workload estimate gives procurement a baseline for free-tier, committed-use, or custom-rate talks. |
Practical usage estimates
Use this table to estimate tokens per task for common workflows. Multiply tokens per task by your monthly task volume, then plug the numbers into the calculator above.
| Task | Input tokens | Output tokens | Tasks per month | Monthly tokens |
|---|---|---|---|---|
| Email draft or reply | 300 | 150 | 500 | 225,000 input / 75,000 output |
| Customer support ticket summary | 800 | 200 | 2,000 | 1,600,000 input / 400,000 output |
| SEO content brief | 1,500 | 1,000 | 100 | 150,000 input / 100,000 output |
| Code review assistant | 3,000 | 800 | 300 | 900,000 input / 240,000 output |
| Internal knowledge-base Q&A | 2,000 | 400 | 1,500 | 3,000,000 input / 600,000 output |
| Data analysis report | 4,000 | 1,500 | 50 | 200,000 input / 75,000 output |
Task calculator
Estimate cost for a single workflow
Pick a common task, set your monthly volume, and see how input and output tokens translate into cost across providers.
Cost optimization
How to reduce API costs
Small changes in model choice, prompt design, and architecture can cut API spend by 50–90% without hurting quality. Focus on the biggest levers first.
Use the smallest model that fits the task
Route simple classification or summarization to a small model. Reserve large models for reasoning, coding, and complex writing.
Cache repeated prompts
If users ask the same questions or run the same reports, cache embeddings and responses instead of calling the API every time.
Shorten and structure outputs
Ask for bullet points, JSON, or strict length limits. Shorter outputs mean fewer output tokens and lower cost.
Batch requests where possible
Many providers offer discounts for asynchronous batch processing. Use it for non-urgent jobs like indexing or reporting.
Trim context windows
Only include the documents and conversation history the model actually needs. Long contexts inflate input token counts.
Measure before optimizing
Track cost per task and quality scores together. A cheaper model is only a win if output quality stays acceptable.
FAQ
How many tokens are in a word?
Roughly 1 token ≈ 0.75 words in English. A 500-word prompt is about 650–750 tokens.
Why does output cost more than input?
Most providers price output tokens higher because generating text is more compute-intensive than reading context.
Does this include free tiers?
No. The calculator shows pay-as-you-go API costs. Deduct any free-tier credits separately.
Why does the calculator show such a wide price range?
Models price input and output tokens very differently, and a high-volume workload amplifies those differences.
Are these prices exact?
No. Prices are rounded and based on standard API tiers. Always check the provider's current pricing page before committing.
How do I reduce my LLM API bill?
Use a smaller model for simple tasks, cache repeated prompts, shorten outputs, and batch requests where possible. See the cost optimization section above for a full breakdown.
What is the difference between input and output tokens?
Input tokens are the prompt the model reads; output tokens are the response the model generates. Output is usually more expensive.
Does the calculator include caching or batch discounts?
No. It shows base pay-as-you-go pricing. Apply discounts separately based on provider terms.
How accurate is token estimation?
Roughly 1 token ≈ 0.75 words in English. Longer prompts, code, and non-English text may vary.
Can I use this for self-hosted models?
No. Self-hosted costs depend on hardware, utilization, and electricity, which this API calculator does not model.