---
id: blog-how-to-estimate-llm-token-costs
slug: how-to-estimate-llm-token-costs
date: 2026-04-24
title: "How to Estimate LLM Token Costs Before You Ship"
summary: "A practical guide to forecasting LLM costs: the token formula, real-world examples across GPT-5.4, Claude, and Gemini, and a free calculator to run the numbers."
categories: ["Guides"]
image:
  src: "/blog/how-to-estimate-llm-token-costs.png"
  alt: "How to Estimate LLM Token Costs Before You Ship"
  width: 1672
  height: 941
---

The first time a bill from OpenAI or Anthropic lands, most teams have the same reaction: "how did we spend that?" Not because the providers hid anything — the pricing is public — but because cost per token is a fiddly unit. A 1,200-token prompt feels abstract until it becomes 10 million of them in a month.

This post walks through how LLM costs actually add up, shows side-by-side math for common workloads, and points you at our free [Token Cost Calculator](/token-cost-calculator) so you can plug in your own numbers before you ship.

## The Token Cost Formula

Every major provider charges the same way: **a price per 1 million input tokens** and **a price per 1 million output tokens**. Output tokens usually cost 4–8x more than input.

```
cost_per_request
  = (input_tokens  / 1_000_000) * input_price
  + (output_tokens / 1_000_000) * output_price
```

Roughly, **1 token ≈ 0.75 English words**. A 1,000-word prompt is ~1,330 tokens. A 500-word response is ~670 tokens.

### Three numbers you need

Before you can estimate anything, get three numbers:

1. **Average input tokens per request** — prompt + system + any context/RAG chunks
2. **Average output tokens per request** — what the model writes back
3. **Requests per day** — multiplied out to the month

If you don't know (2) or (3) yet, estimate from a prototype: log 100 real requests, average them, and extrapolate. Rough numbers now beat precise numbers later.

## Real Costs, Side by Side

A realistic chatbot request: **1,000 input tokens, 500 output tokens**. Prices as of April 2026:

| Model                     | Cost per request | 10K/day | 1M/day  |
| ------------------------- | ---------------- | ------- | ------- |
| **Claude Opus 4.7**       | $0.01750         | $175.00 | $17,500 |
| **GPT-5.4**               | $0.01000         | $100.00 | $10,000 |
| **Gemini 3.1 Pro**        | $0.00800         | $80.00  | $8,000  |
| **Claude Sonnet 4.6**     | $0.01050         | $105.00 | $10,500 |
| **GPT-5.4 Mini**          | $0.00300         | $30.00  | $3,000  |
| **Claude Haiku 4.5**      | $0.00350         | $35.00  | $3,500  |
| **Gemini 3.1 Flash Lite** | $0.00100         | $10.00  | $1,000  |
| **GPT-5.4 Nano**          | $0.00083         | $8.25   | $825    |

A few things jump out from the math, not the marketing:

- The gap between flagship and budget tiers is **20x**, not 2x. A model choice that "doesn't matter for the prototype" becomes a six-figure line item at scale.
- Output tokens dominate total cost on any chat workload. If you're optimizing, cut response length before you cut prompt length.
- Mid-tier models (Sonnet 4.6, GPT-5.4 Mini, Gemini 3.1 Flash Lite) are where the quality-to-cost ratio is best for most real apps.

## Where Estimates Go Wrong

Four things that quietly inflate real bills beyond the estimate:

**1. System prompts count.** Every request pays for the full system prompt. A 2,000-token system prompt adds $0.005 per request on GPT-5.4 — trivial once, $50,000/year at 10 requests per second.

**2. RAG chunks are huge.** Retrieval-augmented apps commonly inject 4–8k tokens of context. That input can outweigh the user's message by 10x.

**3. Tool calls multiply requests.** An agent that calls three tools and reads their outputs is four LLM round-trips, not one. Multiply the per-request cost by the average depth.

**4. Retries and failures.** Provider errors, timeouts, and schema validation retries all cost the same as successful requests. Budget 5–15% overhead.

A good rule of thumb: take your naive estimate and multiply by 1.5 to 2.0. If the real bill comes in under, great. If it comes in over, you already planned for it.

## Use the Calculator

Plugging those three numbers (input tokens, output tokens, requests/day) into a spreadsheet works. It's also tedious to redo every time a new model ships.

We built a [Token Cost Calculator](/token-cost-calculator) that:

- Covers 200+ models across 40+ providers with up-to-date pricing
- Lets you compare official provider rates side-by-side
- Shows what the same workload costs on cheaper providers for the same model (DeepSeek, Groq, Cerebras, and others often undercut the official price)
- Generates a shareable link so you can send estimates to your team

No signup required. Try it: **[llmgateway.io/token-cost-calculator](/token-cost-calculator)**.

## Three Ways to Lower the Bill Without Lowering Quality

Once you can estimate the cost, you can attack it. Three high-leverage moves:

### 1. Cache identical requests

Many production workloads repeat: FAQ bots, classification, batch pipelines, dev/test. A cache hit costs nothing. LLM Gateway caches responses in Redis with per-project TTLs from 10 seconds to a year — teams see 30–90% hit rates on suitable workloads. See [how caching works](/blog/prompt-caching-explained).

### 2. Route to the cheapest healthy provider

The same model is often sold by multiple providers at different prices. DeepSeek-V3, Llama 3.3, Qwen, and others are available across Together, Fireworks, DeepInfra, Groq, and more — sometimes at half the price of the original. A gateway with smart routing picks the cheapest provider that meets your uptime and latency bar, automatically. See [cut LLM costs with request routing](/blog/cut-llm-costs-with-request-routing).

### 3. Match the model to the task

Don't call GPT-5.4 to classify sentiment. A three-tier setup — Nano or Flash Lite for classification, Haiku or Mini for structured extraction, flagship for reasoning — typically cuts total spend 60–80% with no user-visible quality drop.

## TL;DR

- Cost per request = `(input_tokens / 1M) * input_price + (output_tokens / 1M) * output_price`
- Output tokens dominate. Shorten responses before you shorten prompts.
- Flagship vs budget is ~20x. Pick the cheapest model that still meets quality.
- Multiply naive estimates by 1.5–2x to account for system prompts, RAG, tools, retries.
- Caching and smart routing are the two biggest levers — both are free on [LLM Gateway](/signup).

**[Try the Token Cost Calculator →](/token-cost-calculator)**
