AI Latency Calculator
Estimate AI response latency.
Formula
Latency = TTFT + Output/TPS
Example
2K in, 500 out, fast model → 6.5s total.
Understanding the AI Latency
AI cost and capacity calculators help builders avoid surprises when production usage scales. The ai latency calculator turns model and usage parameters into a number - tokens, cost, throughput, hardware requirement - that you can use to plan capacity, budget, or model selection.
How it actually works
Estimate AI response latency.
The formula is straightforward arithmetic once the inputs are correct; the value of the calculator is in handling the algebraic manipulation reliably and removing transcription errors. Plug in your specific inputs above and the result appears as you type, so you can immediately see how each variable affects the answer.
What the numbers really say
GPT-4o at $2.50 per million input tokens and $10 per million output tokens, processing 100,000 chat requests per month with 500 input and 500 output tokens average, costs $625/month. The same workload on a smaller model (GPT-4o mini at $0.15 in / $0.60 out) costs $37.50/month - 17x cheaper. Model choice has dramatic cost implications at scale.
The deeper context most users miss
AI cost calculation has an additional dimension most software cost models do not: the inputs themselves are user-generated and unpredictable. A chat application's token usage depends entirely on how users actually engage - which is difficult to forecast in advance and varies enormously across user segments. Power users can generate 10-100x the token consumption of typical users. This is why production AI applications usually implement rate limits, context window caps, and aggressive caching strategies. The calculator gives you per-request cost; the harder problem is forecasting how many requests will happen and how large they will be.
What people get wrong
- Forgetting input vs output token pricing. Output tokens typically cost 3-5x more than input.
- Underestimating output length. A 100-token prompt asking for a 2000-token response costs ~20x what the prompt itself costs.
- Ignoring caching discounts. Many providers offer steep discounts on cached prompt prefixes.
- Not budgeting for retries and exploration. Production usage is always 1.3-2x what naive math suggests.
When this calculator helps most
The ai latency calculator is most useful when you are making a real decision - comparing options, sizing a commitment, sanity-checking a quote, or planning ahead. The output is precise to your inputs; the inputs themselves are the place to slow down. Spend extra time on the assumptions you are making about rate, term, timing, or context-specific variables - those swing the answer far more than the formula's arithmetic does. A 5% change in the input often produces a 10-20% change in the output, which means small input errors compound into large output errors.
Where the math comes from
Provider pricing is published on each company's documentation site (OpenAI, Anthropic, Google, Cohere). Token counting libraries like tiktoken (OpenAI) and tokenizers (Anthropic) give exact counts. Academic AI cost analysis comes from Stanford HAI, MIT, and the Berkeley AI lab.
Questions and answers
Are these prices current?
Provider pricing changes regularly. Re-check the official documentation before making capacity decisions. Pricing on this calculator reflects published rates at the time of the last review.
Why do output tokens cost more?
Output generation is more expensive computationally - autoregressive token-by-token generation. Input is processed once in parallel.
How do I count tokens?
Use the provider's tokenizer (tiktoken for OpenAI, similar for others). Rough rule of thumb: 1 token ~ 0.75 words in English. Specialized content (code, JSON) tokenizes differently.
Should I use a smaller model?
Smaller models are dramatically cheaper and often sufficient. Test on your specific use case; quality often plateaus before cost does.
How do caching discounts work?
Anthropic's prompt caching, OpenAI's prompt caching: cached prefix tokens are reused at lower cost. Useful when many requests share long initial context (system prompts, RAG context). Discounts of 50-90% on cached portions.
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