LLM Cost Comparison

The LLM cost comparison calculator is a budgeting tool that estimates how much you will pay each month to run a large language model (LLM) workload across ten popular models from OpenAI, Anthropic, Google, Meta, and Mistral. Instead of opening five different pricing pages and copying numbers into a spreadsheet, you enter three usage parameters and the calculator instantly ranks every model from cheapest to most expensive, highlights the lowest-cost option, and shows the dollars you could save by switching providers.

The three inputs you provide are the average input tokens per request (the prompt you send, including system instructions, retrieved context, and the user message), the average output tokens per request (the completion the model generates back), and the number of requests per month (your monthly API call volume). These three values capture the dominant drivers of an API bill, because nearly every commercial LLM provider charges separately for tokens read in and tokens written out.

This calculator is built for product managers estimating a feature's gross margin, founders modeling unit economics, engineers comparing GPT-4 vs Claude vs Gemini, and finance teams forecasting AI spend. Because output tokens almost always cost two to five times more than input tokens, the tool makes it obvious that a chat assistant that writes long answers behaves very differently on price than a classification job that emits a single word. By laying all ten models side by side with identical assumptions, the LLM pricing calculator turns a fuzzy "which AI model is cheapest" question into a concrete, sortable dollar figure you can defend in a planning meeting.

Pricing in this tool is expressed per 1,000 tokens, matching the convention most provider documentation used when these published rates were captured. The model list spans frontier reasoning models such as GPT-4 and Claude 3 Opus, balanced mid-tier models like Claude 3 Sonnet and Gemini 1.5 Pro, and ultra-cheap workhorses including GPT-3.5 Turbo, Claude 3 Haiku, and Gemini 1.5 Flash, so you can find a price point that fits any tier of quality requirement.

The calculator uses a two-step formula. First it computes the cost per request by converting your token counts into thousands and multiplying by each model's per-1K input and output prices. Then it multiplies that per-request cost by your monthly request volume to produce the monthly cost. Because token counts are divided by 1,000, the published prices act as a rate "per thousand tokens" exactly as the provider rate cards list them.

Every model is scored with the same three inputs, so the ranking is a fair apples-to-apples comparison. The tool then sorts the ten results ascending, labels the first entry the cheapest option, labels the last entry the most expensive, and reports potential savings as the difference between those two monthly totals. That savings number answers the practical question: how much money is left on the table if you keep paying for the priciest model when a cheaper one could do the job?

Notice that the input-versus-output split matters enormously. A model like Claude 3 Opus charges $0.015 per 1K input tokens but $0.075 per 1K output — five times more for what it writes. So workloads that generate long outputs (essays, code, summaries) are dominated by the output price, while retrieval-heavy or classification workloads with short answers are dominated by the input price. Adjust the two token sliders to see this trade-off shift the leaderboard.

The calculator compares the ten models below. Prices are shown per 1,000 tokens, split into input (prompt) and output (completion) rates, exactly as the tool applies them in its math. Use this table to understand why a model lands where it does on the leaderboard before you ever change an input.

A few patterns jump out. Gemini 1.5 Flash is the cheapest model in the set by a wide margin and is almost always the leaderboard winner for high-volume, latency-tolerant tasks. Claude 3 Haiku is the next-cheapest and often competitive on quality. At the premium end, Claude 3 Opus carries the highest output price in the list, which is why output-heavy workloads make it the most expensive option. Llama 3 70B is unusual because it charges the same rate for input and output, which makes it predictable for workloads with a balanced read/write ratio.

Understanding the input/output split is the single most useful skill when reading any LLM cost comparison. Input tokens are everything the model reads: your system prompt, few-shot examples, retrieved documents from a RAG pipeline, conversation history, and the user's current question. Output tokens are only what the model generates in response. Providers price output higher because generating each new token requires a full forward pass through the network, whereas input tokens can be processed more efficiently in a single batched prefill.

This asymmetry has direct design consequences. If your application sends a 10,000-token document and asks for a one-sentence summary, your bill is dominated by input. If it sends a short instruction and asks the model to write a 2,000-word article, your bill is dominated by output. The calculator exposes both knobs so you can model either regime precisely. A common mistake is to assume that trimming the prompt always saves the most money; for generation-heavy products, shortening or capping the output via a max-tokens limit often saves far more.

Retrieval-augmented generation and long-context chat both inflate input tokens, sometimes dramatically, because the entire conversation or document set is re-sent on every turn. Caching, prompt compression, and summarizing old turns are the standard levers to fight that growth. On the output side, asking for concise answers, using JSON schemas to bound length, and switching long-form tasks to a cheaper model are the highest-impact moves. Plug realistic numbers into the calculator for each scenario and the ranking will tell you whether a prompt-side or output-side optimization pays off more for your specific workload.

The cheapest model on paper is not always the right choice — but the LLM pricing calculator gives you the cost axis you need to make a deliberate trade-off against quality. A practical strategy is tiered routing: send simple, high-volume requests to an inexpensive model such as Gemini 1.5 Flash, Claude 3 Haiku, or GPT-3.5 Turbo, and reserve a frontier model like GPT-4, Claude 3 Opus, or Gemini 1.5 Pro for the small fraction of requests that genuinely need stronger reasoning.

To use the tool for this decision, estimate the token profile of each request type separately. Classification and extraction tasks usually have moderate input and tiny output, so a cheap model wins decisively. Drafting, coding, and summarization tasks have large output, so the output price dominates and the savings from a cheaper model are even larger. Run the calculator twice — once with each token profile — and compare the cheapest-option figure to the cost of your preferred premium model to see the exact premium you are paying for quality.

Remember that this calculator models direct token pricing only. Real production costs may also include hosting or infrastructure for open-weight models like Llama 3 70B (where the listed rate reflects a typical inference-provider price rather than a first-party API), data egress, fine-tuning, embeddings for retrieval, and rate-limit overage. Treat the monthly figure as the variable API cost of inference, then layer your fixed costs on top. Used this way, the LLM cost comparison calculator becomes the foundation of a complete AI budget rather than the whole story.