A million tokens is not just an abstract number — it is the currency of modern AI. A recent article on Habr looks closely at how large models and the price per million tokens are redefining the way developers estimate AI expenses. The material provides a compact but deep overview of the factors that influence token rates, and why understanding them is critical for anyone who builds products on large language models. This article expands on that analysis with practical examples, step-by-step calculation methods, and a checklist that helps keep inference budgets under control.
When the first large conversational models entered the market, pricing was a simple multiplier: the more tokens, the higher the bill. Today, in 2026, the landscape has become considerably more nuanced. Providers differentiate between input and output, introduce cache discounts, charge for special capabilities, and scale prices according to model size and complexity. This makes a single “price of a million tokens” misleading — instead, you need to understand the full pricing model.
What Is a Token and Why Does a Million Matter?
Before diving into pricing, it is helpful to recall what a token is. Most modern language models do not read plain text character by character; they split it into tokens, which can be short word fragments, whole words, or punctuation marks. One token corresponds roughly to four characters in English or about 0.75 words. A million tokens is approximately the volume of 750,000 English words — this is roughly the text of two or three full-length novels. While a human can read such a corpus in days, a model can process it in seconds and charge for it by the token.
For developers, the per-million-token price is the natural unit for comparing models and providers. It is also the easiest number to estimate from API logs: every response returns a usage breakdown with prompt_tokens and completion_tokens. Multiplying those numbers by the published rate gives the exact cost of one request.
The Economic Logic of Large Model Pricing
The Habr article examines the economic factors behind the price per million tokens for large models. It highlights that the final rate depends on model architecture, serving infrastructure, and the provider’s pricing strategy. Training a frontier-scale model involves thousands of powerful accelerators running for months, and these capital expenditures are amortized through usage fees. In addition, each inference request consumes memory bandwidth, floating-point computations, and, in many cases, sophisticated orchestration between multiple GPU nodes.
Large models are usually priced higher than their smaller counterparts because they require more memory and compute per token. However, the gap is not always linear. Sometimes a model with ten times more parameters costs only a few times more per token — this is the result of optimization work, quantization, and new serving technologies such as continuous batching and speculative decoding.
The material also notes that the price per million tokens is not purely a hardware cost. It includes the provider’s research and development, compliance, and support costs. This is why two models with similar parameter counts can have very different prices: one may include an enterprise SLA, while another targets the open-source community with a break-even pricing model.
Input Tokens, Output Tokens, and Cached Tokens
Modern APIs rarely offer a single flat rate. The vast majority of providers charge different prices for input and output tokens. Input tokens are the prompt you send; output tokens are the generated response. In most architectures, generation is more expensive because the model must produce each token sequentially, while processing the prompt can be done in parallel. Hence, providers often set the output price several times higher.
Another crucial component is caching. When an application sends similar system instructions or long few-shot examples with every request, a smart cache can store intermediate representations of these parts. Cached input tokens are typically two to four times cheaper than regular input tokens. The Habr material points out that optimizing for cache is one of the most straightforward ways to reduce the total price per million tokens.
Specific providers implement caching differently. For example, the OpenAI API offers automatic prompt caching, and a service like ASI Biont, which supports integration with OpenAI and other LLM APIs, helps developers centralize their token accounting across models. ASI Biont supports connections to OpenAI API and other providers via its API — see asibiont.com/courses for details.
Practical Guide: How to Calculate the Cost of a Million Tokens
Although the “price per million tokens” is already a standard unit, applying a price sheet to a real product requires arithmetic. Here is a simple step-by-step approach.
- Estimate your usage volume. Determine the average number of input and output tokens per API call. Your tests can measure this by reading the usage fields returned by the API.
- Determine your monthly load. Multiply the average number of calls per day by 30.
- Choose a pricing tier. Most providers publish their prices per million tokens for input and output separately.
- Apply the formula.
Let us look at a hypothetical example. Suppose a provider charges $2 per one million input tokens and $10 per one million output tokens. One task uses 4,000 input tokens and 500 output tokens. The cost per call is: (4,000/1,000,000) × $2 + (500/1,000,000) × $10 = $0.008 + $0.005 = $0.013. If your service handles 100,000 such calls per month, the total cost is $1,300. This example shows why precise token accounting matters: even small changes in the prompt length change the final invoice dramatically.
Here is a Python function that automates the calculation:
def estimate_monthly_cost(monthly_calls, input_tokens, output_tokens,
input_price, output_price):
input_cost = monthly_calls * input_tokens / 1_000_000 * input_price
output_cost = monthly_calls * output_tokens / 1_000_000 * output_price
return input_cost + output_cost
# Hypothetical pricing: $2/M input, $10/M output
total = estimate_monthly_cost(100_000, 4000, 500, 2, 10)
print(total) # 1300.0
The function takes the monthly number of requests, the average token counts, and the provider rates. It returns the expected total invoice. You can also extend it to multiple models and add caching discounts by multiplying cached input tokens by a factor between 0.25 and 0.5.
Practical Ways to Reduce the Price of a Million Tokens
The first rule is to shorten prompts. Every unnecessary sentence in the system prompt is multiplied by every request. Moving static content to a cached prefix is a widely used technique because cached tokens cost less per million than uncached input tokens. The second rule is to use the smallest model that solves the task. For classification, extraction, or routing, a compact model often costs ten times less than the largest one. The third rule is to control the output token budget. Many APIs support a max_tokens parameter; setting it too high can force the generation to continue pointlessly, while setting it too low can crash the task mid-sentence.
Another effective tactic is to combine models. A small model can pre-route and compress requests before the large model writes the final answer. This style of multi-stage inference uses the expensive large model only where it is really needed. The table below lists the factors that influence token prices and the levers available to developers.
| Factor | Effect on price | How to reduce |
|---|---|---|
| Input token volume | Direct linear cost | Compress prompts, use smaller contexts |
| Output token volume | Usually the most expensive | Set max_tokens, use structured output |
| Model size | Bigger model leads to higher cost | Distill task-specific smaller models |
| Context length | Cost grows with context size | Load only relevant context, use retrieval |
| Caching | Cached tokens cost less | Reuse static prompts, few-shot examples |
| Serving mode | Real-time inference is premium | Consider batch or async modes |
A practical budget checklist for a new AI feature includes: measure the average prompt length in real usage; trace the distribution of output lengths, because outliers will dominate the bill; evaluate one smaller model in the same model family; turn on prompt caching in the API; and set a monthly token limit using the provider’s quota system. These steps generally reduce the effective price of a million tokens by 50–80 percent without sacrificing quality.
What the Article on Habr Means for the Future
The material concludes that the price per million tokens will continue to fluctuate. The key trend is not a simple price drop, but the increasing differentiation of rates. In 2026, companies can choose between premium frontier models, budget-friendly compact models, local open-source models, and hybrid approaches. The winner is the one who aligns the mathematical model of token costs with business value.
The article also emphasizes that price lists alone are not enough to compare models. Looking carefully at cache availability, context-window sizes, and output token limits can change the total cost of ownership by a large factor. Transparency in pricing, in turn, allows developers to trust the APIs they integrate and to plan products with predictable margins.
Conclusion
Understanding the price of a million tokens is no longer a niche expertise — it is a core financial skill for product teams working with AI. The Habr article provides a solid entry point for examining how large models are priced, and the approach described in this guide helps translate that information into operational decisions. Start by measuring your token consumption, then experiment with cache, model selection, and prompt compression. The cost of a million tokens is the starting line, not the finish line.
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