LLM Token Cost Calculator

What Is the LLM Token Cost Calculator?

The LLM Token Cost Calculator works out the cost of a single API call to a large language model, or the compounding cost of an entire multi-turn conversation, using whatever input, output, and cached-token rates you enter for your specific provider and model. Most LLM pricing calculators simply multiply token counts by a fixed price list, which goes stale within weeks given how often providers such as OpenAI, Anthropic, and Google revise pricing, a pattern documented across dozens of API pricing changes tracked in a 2026 breakdown of OpenAI's evolving API pricing. Figure out your own current published rate before entering it here, since this calculator is deliberately built around user-supplied prices rather than a hardcoded table that would mislead you the moment a provider changes its pricing page.

Look into which mode fits your question: single call mode handles one request, including a Batch API discount toggle for asynchronous jobs, while multi-turn conversation mode models a genuinely different and frequently underestimated problem, the way a chat's total cost accelerates because every turn resends the full conversation history rather than just the newest message.

Why Multi-Turn Conversations Cost More Than N Single Calls

Because most LLM APIs are stateless, a chat application must resend the entire conversation history, system prompt, every prior user message, and every prior assistant reply, on every single turn. This means input token usage grows with each turn, and total conversation cost accelerates well beyond what a naive "turns times average cost" estimate would suggest. One well-documented case, described in a practitioner's breakdown of proven AI API cost-cutting strategies, saw a founder's monthly API bill jump from $200 to $6,000 with no obvious single cause, a pattern that fits exactly this kind of unaccounted-for context accumulation across many concurrent conversations.

Come back to this table whenever a conversation feature's costs look larger than expected, since a 10-turn conversation resends nearly 4,000 tokens of pure history on its final call alone, on top of whatever new content that turn adds.

Prompt Caching and Batch API Discounts: How Much You Can Actually Save

Prompt caching charges a steep discount for input tokens the provider has already processed and can reuse rather than recompute, which directly targets the repeated-history problem described above. According to Anthropic's own prompt caching documentation, cache hits cost roughly 90% less than standard input tokens, though writing a new cache entry itself costs a premium, 1.25 times the base input rate for a 5-minute cache and 2.0 times for a 1-hour cache, before subsequent reads drop to that steep discount. In one documented case, described by a practical cost-optimization writeup comparing Claude, GPT, and Gemini caching, adding prompt caching to a production agent brought its monthly bill down from $720 to $72, a 90% reduction achieved purely by letting the stable, repeated portion of each request hit the cache instead of being billed at full price every time. Work out whether your framework or SDK auto-applies caching or requires explicit cache markers, since the saving only materializes if the repeated prefix is actually recognized and reused turn to turn.

Batch APIs offer a separate, stackable discount. As OpenAI's own Batch API FAQ confirms, batch jobs receive a flat 50% discount on both input and output tokens across every model, in exchange for asynchronous processing completed within a 24-hour window rather than an immediate synchronous response. Batch discounts and cache discounts can combine on some providers, so a cached, batched request can end up costing a small fraction of the equivalent synchronous, uncached call.

Estimating Monthly and Yearly AI Spend

Carry out a monthly or yearly projection by multiplying a single call's cost by expected daily call volume rather than guessing at a round number, since per-call costs that look trivial in isolation, a fraction of a cent, can add up to a meaningful monthly bill once multiplied across thousands of daily calls. On top of that, project conversation-based features using the multi-turn total rather than a single-call estimate, given that a chat feature's real per-user cost is the full accumulated conversation cost, not the cost of one isolated exchange. Pull out per-model pricing directly from each provider's own pricing page before running a projection, since Anthropic's published pricing page lists separate rates for every model tier, and mixing up which tier a projection is actually based on is a common, easily avoidable source of budget error.

Accuracy and Limitations

The arithmetic in both modes is exact given accurate token counts and current provider rates. This calculator does not count tokens from raw text itself, since that requires each provider's specific tokenizer, and it does not track or predict future pricing changes, since providers revise per-token rates on their own schedule with no advance public commitment to a fixed rate, a volatility the same 2026 OpenAI pricing breakdown documents across several rounds of published rate changes within a single year. The conversation mode also assumes a fairly regular pattern of tokens added per turn; a real conversation with highly variable turn lengths will differ from this simplified average, though the underlying compounding mechanic it demonstrates, full history resent every turn, holds regardless of how evenly sized individual turns actually are.

The Most Common LLM Cost Estimation Mistake

The mistake I see most often is budgeting a chat feature using the cost of one representative exchange, multiplied by an expected number of conversations, while ignoring that each conversation itself accumulates cost turn over turn. With that in mind, always model a full conversation's compounding cost, not a single turn's cost, before setting a per-user or per-feature budget, since the gap between those two numbers grows directly with how many turns a typical session actually runs. This turns up most often in support chatbots and coding assistants, exactly the product categories where long, multi-turn sessions are the normal case rather than the exception, and exactly the pattern behind the cost-cutting case studies cited earlier. Once a per-conversation cost is estimated realistically, our Budget Calculator can help fold that recurring AI spend into a broader monthly budget.