Token cost calculator: how to estimate your real LLM spend before it surprises you
LLM pricing looks cheap until you multiply it by your actual request volume. Here's how to calculate your real token costs, common mistakes that inflate your bill, and a free calculator that does the math for you.
NR
NeuralRouting Team
April 22, 2026
Token cost calculator: how to estimate your real LLM spend before it surprises you
"$2.50 per million tokens" sounds cheap. Then you process 200,000 requests per day, each averaging 800 input tokens and 400 output tokens, and your monthly bill is $7,500. For one endpoint.
Most teams underestimate their LLM costs because they look at per-token pricing without multiplying it by actual volume. The per-unit cost is low. The aggregate cost isn't. The calculation is simple. The surprise comes from volume.
The math is simple. The surprise comes from the volume.
Where the estimates break down
Output tokens cost more than input tokens
With GPT-4o, output tokens cost 4x more than input tokens. With Claude Sonnet 4.6, it is 5x. With Claude Opus, also 5x.
This means a request that generates a 500-word response (roughly 650 tokens) costs more in output alone than a 2,000-word input prompt costs in input. Most cost estimates focus on input tokens because the prompt is the thing you control. But the output is where the money goes.
If your LLM generates verbose responses and you have not set max_tokens or asked for concise answers, you are overpaying on every single request.
System prompt costs add up fast
Your system prompt gets sent with every request. If it is 500 tokens and you make 100,000 requests per day, that is 50 million input tokens per day just for the system prompt. On GPT-4o, that is $125/day or $3,750/month, purely for instructions the model reads over and over.
Trimming a 500-token system prompt to 200 tokens saves $2,250/month at that volume. This is why prompt optimization is not a micro-optimization. At scale, every unnecessary sentence in your system prompt has a dollar cost.
Retries and errors inflate your real volume
If 5% of your requests fail and retry, your actual request volume is 5% higher than your application metrics show. Some retry strategies send the same request 3 times before giving up. That is a 15% cost overhead that does not show up in your application logs but absolutely shows up on your API bill.
Function definitions add hidden token costs
If you use function calling or tool use, the function definitions are included in the input tokens on every request. A set of 10 function definitions can easily add 1,000-2,000 tokens to every request. At 100,000 requests/day on GPT-4o, 1,500 extra tokens per request costs $375/day or $11,250/month.
Some teams define 20+ functions and only use 2-3 of them in any given request. Trimming the function set per endpoint is an easy win.
Quick cost estimation table
Here is what different usage levels cost across popular models, assuming a 2:1 ratio of input to output tokens:
Daily Requests
Avg Tokens/Req
GPT-4o
GPT-4o-mini
Claude Sonnet 4.6
DeepSeek V3.2
1,000
1,000
$15/mo
$0.90/mo
$18/mo
$0.42/mo
10,000
1,000
$150/mo
$9/mo
$180/mo
$4.20/mo
100,000
1,000
$1,500/mo
$90/mo
$1,800/mo
$42/mo
100,000
2,000
$3,000/mo
$180/mo
$3,600/mo
$84/mo
500,000
1,500
$11,250/mo
$675/mo
$13,500/mo
$315/mo
The gap between GPT-4o and DeepSeek V3.2 at 500K requests/day is $11,250 vs $315. That is a 35x difference.
The question isn't "which model is cheapest?" It's "which of my requests can use the cheap model, and which ones actually need GPT-4o?"
The hidden cost: Model Tax
Every online calculator does the same thing: you pick a model, enter token volume, get a monthly cost. It's useful, but incomplete.
The number you should actually care about is the gap between what you spend now and what you would spend if you routed each request to the cheapest model capable of handling it.
If you send everything to GPT-4o and your bill is $5,000/month, but 70% of those requests could run on DeepSeek or GPT-4o-mini with identical output, your real cost should be closer to $1,500/month. The $3,500 difference is what we call the Model Tax, the invisible cost of not routing by complexity.
That is the calculation that actually matters for optimization. Not "how much does GPT-4o cost per token" but "how much am I wasting by using GPT-4o on requests that don't need it."
How to audit your current spend
If you want to calculate your own Model Tax, here is the process:
Step 1: Export your API logs. Pull a week of requests with token counts per request. Most providers have usage dashboards that can export this.
Step 2: Categorize by task type. Group your requests by what they actually do: classification, extraction, Q&A, summarization, generation, reasoning. You can often infer this from the endpoint or the system prompt.
Step 3: Estimate the minimum viable model for each category. Classification and extraction almost always work on economy models. Summarization and basic Q&A work on mid-tier models. Complex generation and reasoning need premium models.
Step 4: Calculate the routed cost. Multiply each category's volume by the appropriate model's pricing. Sum it up. Compare to your current bill.
Most teams find that 60-80% of their requests can use a cheaper model. The savings are between 60% and 85% of current spend.
Or just use the calculator
I built a calculator that does this math for you. Plug in your monthly spend and request volume, and it shows exactly how much you are overpaying and what your bill would look like with intelligent routing.