LLM Comparison Calculator

Understanding the LLM Comparison Calculator

An LLM cost calculator compares two models on token pricing across a monthly request volume. It captures the input and output price asymmetry that dominates real bills, and it omits several factors that frequently matter more than the headline rate.

How it actually works

Enter input and output prices per million tokens for two models, plus average token counts and daily requests. The calculator computes monthly cost for each and reports the difference. A model at $3 input and $15 output against one at $0.50 and $1.50, with 1,000 input and 500 output tokens across 1,000 daily requests, costs $315 against $37.50 monthly.

The deeper context most people miss

Output tokens typically cost three to five times input tokens, which means a workload producing long responses is priced very differently from one producing short ones, even at identical total token counts. Controlling output length is frequently the single most effective cost lever available, and it is easier to change than the model.

Why output costs more than input

The asymmetry reflects a genuine difference in how the two phases work. Processing input, the prefill phase, handles all tokens in a single parallel pass, which uses hardware efficiently. Generating output, the decode phase, produces one token at a time, each requiring the entire model's weights to be read from memory for a single step, which makes it memory-bandwidth-bound and far less efficient per token. Providers price accordingly. Several consequences follow. Instructing a model to be concise has a direct and disproportionate effect on cost. Structured output formats such as JSON produce fewer tokens than prose explanations for the same information. Requests for step-by-step reasoning in the visible output multiply cost, and reasoning models that generate extensive internal reasoning are priced accordingly, sometimes billing reasoning tokens at output rates, which can make a nominally similar price per million far more expensive in practice. On the input side, prompt caching offered by several providers charges a reduced rate for a repeated prefix, which is substantial for applications sending a large system prompt or document with every request, and it changes the arithmetic enough that a model with caching can beat a cheaper one without it. Batch APIs offer discounts for asynchronous processing, typically around half price, which suits offline workloads where latency does not matter.

A worked example: what the comparison misses

Model A costs $315 monthly against Model B's $37.50, an eight-fold difference, and the decision is not therefore obvious. Quality is the omitted variable, and it is not linear with price: for many tasks the cheaper model performs adequately, and for some it does not, with the gap concentrating in complex reasoning, long-context work, instruction following under ambiguity, and reliability of structured output. The right approach is to test both on a representative sample of your actual workload rather than relying on benchmarks, since published benchmark performance correlates imperfectly with performance on any specific task. Beyond quality, several cost factors sit outside the calculation. Retries and failures consume tokens without producing usable output, and a less capable model requiring more retries can cost more in practice than its rate suggests. Longer prompts required to steer a weaker model add input tokens on every request. Human review and correction of poor output has a labour cost that dwarfs token pricing. Latency differs and affects user experience. Rate limits differ and constrain throughput. Context window size determines what is possible at all. And the reliability of structured output matters enormously for anything programmatic, since a model that produces malformed JSON five percent of the time creates engineering work that no price difference compensates for.

Deciding how to reduce cost without changing model

Several levers frequently outperform switching models. Reducing output length is the largest given output pricing, and it is achieved through explicit instruction, output format constraints, and setting maximum token limits. Reducing input length helps proportionally less but still matters, particularly where a large context is sent repeatedly, and prompt caching addresses exactly that case. Routing is the highest-leverage architectural change: sending simple requests to a cheap model and escalating only complex ones to an expensive model can capture most of the quality at a fraction of the cost, and classification of request difficulty is itself a cheap operation. Caching complete responses for repeated identical queries eliminates cost entirely for those requests, and many workloads have more repetition than expected. Batching offline work through discounted batch endpoints roughly halves cost where latency is tolerable. Fine-tuning a smaller model on task-specific data can bring it to acceptable quality at a fraction of the inference cost, though it adds training cost and operational complexity and locks in a model version. Reducing the number of sequential calls in a chain matters, since each adds full cost, and consolidating several steps into one prompt is frequently possible. Measuring actual token usage rather than estimating is the necessary first step, since assumptions about average lengths are usually wrong.

Why pricing changes faster than architecture

Model pricing has fallen dramatically and repeatedly, with cost per token for equivalent capability dropping by orders of magnitude across successive generations, and this has a specific implication for how systems should be built. Hard-coding a model choice into an application creates switching cost at exactly the moment when switching is most valuable. Abstracting the model behind an interface, so that changing provider or model is a configuration change rather than an engineering project, is a low-cost decision at build time and a high-value one within months. Several libraries and gateway services provide this abstraction. The corollary is that optimising heavily for current pricing may be wasted effort if the price falls before the optimisation pays back, and the calculation of whether to invest engineering time in cost reduction should account for expected price decline. Against this, some costs do not fall: engineering time, latency, and the operational burden of complexity persist regardless of token pricing. There is also a countervailing trend, since capability improvements arrive in more expensive models and workloads tend to expand toward available capability, so total spend frequently rises even as unit cost falls. Monitoring actual spend against a budget with alerting matters more than any point-in-time comparison, since usage growth rather than unit price is what produces unexpected bills.

Variations: reasoning models, caching, and self-hosting

Reasoning models generate extended internal reasoning before answering and bill those tokens, frequently at output rates, so a comparison based on advertised price per million substantially understates their cost for tasks that trigger long reasoning. Their pricing therefore needs measuring on real workloads rather than calculating from rates. Prompt caching charges a reduced rate for repeated prefixes, with implementations differing between providers in cache duration, minimum size, and discount level, and it can change a comparison entirely for applications with large fixed context. Batch processing offers roughly half price for asynchronous work. Self-hosting open-weight models eliminates per-token pricing in exchange for infrastructure cost, and the crossover depends heavily on utilisation, since idle GPU time is pure loss and self-hosting only competes at sustained high volume. Fine-tuning has its own pricing for training and sometimes a premium on inference. Embeddings are priced separately and far lower. Image and audio inputs are priced differently again, often per image or per minute rather than per token. Free tiers and volume discounts complicate direct comparison, and enterprise agreements frequently differ substantially from published rates.

Comparing model costs sensibly

Measure your actual token usage rather than estimating, since assumptions about average input and output lengths are usually wrong and output length in particular drives cost. Weight output tokens heavily, since they typically cost three to five times input and controlling response length is the most effective single lever. Test both models on a representative sample of your real workload rather than relying on published benchmarks, which correlate imperfectly with any specific task. Account for retries, longer steering prompts, and human correction, since a cheaper model needing more of any of these can cost more overall. Consider routing simple requests to a cheap model and escalating only complex ones, which captures most quality at a fraction of the cost. Use prompt caching where a large prefix repeats and batch endpoints where latency is tolerable. Abstract the model behind an interface so switching is configuration rather than engineering, since pricing changes faster than applications do. And monitor spend with alerting, since usage growth causes unexpected bills more often than unit price does.

What people get wrong

• Comparing models on input price alone, when output tokens typically cost three to five times more and frequently dominate the bill.
• Choosing on published benchmarks rather than testing on your own workload, since benchmark performance correlates imperfectly with performance on any specific task.
• Ignoring retries and steering overhead, when a cheaper model requiring longer prompts and more retries can cost more in practice than its rate implies.
• Hard-coding a model choice, when pricing and capability change rapidly and switching should be a configuration change rather than an engineering project.

Where the math comes from

Monthly Cost = ((Average Input Tokens / 1,000,000 × Input Price) + (Average Output Tokens / 1,000,000 × Output Price)) × Daily Requests × 30. Prices are per million tokens. The calculation covers token pricing only and excludes retries, prompt caching discounts, batch pricing, reasoning tokens, rate limits, and the quality difference between models.

Questions and answers

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