LLM API Cost Calculator — What You Actually Pay

Most cost calculators multiply tokens by the sticker price. Production workloads rarely pay sticker price — they pay the effective rate after cache hits, batch discounts and long-context tiering. This calculator applies all of them and ranks every model by real monthly cost.

Presets

List prices per 1,000,000 tokens, verified November 2026. They exclude negotiated discounts, provisioned throughput, storage and fine-tuning fees. Cached and batch rates apply only in the modes that model them — switching mode re-prices every row rather than adjusting the total afterwards.

What this LLM token price estimator actually computes

The same question gets asked four different ways: how much does this LLM API cost, what is the model cost per million tokens, what will my token cost be at a given request volume, and which price estimator can be trusted. This page answers all four, because the answer is the same number every time — the only inputs that change it are the cached share, the batch flag and whether your prompt crosses a long-context threshold.

Set your four workload numbers above and the estimator recomputes every model in the table, sorted by cost per month. Nothing is estimated behind an email gate, and there is no account to create.

How the cost is actually calculated

Total cost is the sum of four independently billed line items, multiplied by your request volume:

The part most calculators skip is the cache write premium. Anthropic bills a 5-minute cache write at 1.25× the input rate and a 1-hour write at 2×. A caching strategy only pays off if you write once and read many times — roughly 20 reads to 1 write breaks even on a 5-minute TTL, more on a 1-hour TTL. Google bills cached context storage separately per MTok-hour rather than per token, so it does not appear as a per-token write rate here.

Long-context tiers

Quoting a single price per model is misleading if your prompts are large. This calculator switches rates automatically when your input size crosses a provider's threshold:

Claude's flat 1M window is a real advantage for whole-document workloads: a 900K-token request bills at the same per-token rate as a 9K one. On the same workload, OpenAI's tiering roughly doubles the effective rate.

How to cut your bill

1. Route by task, not by brand

Matching the model tier to the job is a bigger lever than switching vendors. Classification, extraction, routing and first-pass support belong on a budget tier. Tier 2 of the table above will beat your flagship on most of those jobs without a meaningful quality drop.

2. Cache a stable prefix

Any workload where requests share a system prompt, a policy document or a product catalogue should cache it. Cache reads bill at roughly 10% of list on OpenAI, Anthropic and Google, and about 2% on DeepSeek.

3. Push offline work to batch

Batch halves both input and output. Combined with a cache hit, cached input inside a batch request can land near 5% of list. This is the single largest available reduction for evaluation runs, nightly summaries and bulk enrichment.

4. Remember reasoning tokens bill as output

Thinking and reasoning tokens are billed at the output rate, and output runs 3–6× input across every vendor. Heavy deliberation, not prompt size, is what usually dominates an agent bill. If output price is your problem, the fix is prompting and model choice — not caching.

Deciding when to switch

Run the numbers before you migrate. The pattern most teams land on is a two-model setup rather than a single choice, and the split is almost always the same:

• A budget tier for the boring 80% — routing, classification, extraction, summarisation, first-pass support. This is where request volume lives, and it is where a tier downgrade pays for the expensive model that handles the last 20%.
• A frontier model for the part that is the product — the reasoning a user actually pays for. Volume is low enough here that a 2.5× rate difference does not matter.

Two things that look like cost savings and usually are not:

• Switching vendor for a flat rate when your current model is already one tier down. The tier is a bigger lever than the vendor — the gap between a workhorse and a budget model is 10×; the gap between similarly positioned models at different vendors is usually under 20%.
• Buying a cheaper model to save tokens — cheaper models are not shorter. Output volume is a product decision, not a pricing lever.

And one that usually is: if your input is large and repetitive, the long-context tier is a bigger line item than the model choice. Compare the same workload at 150K and 400K tokens in the calculator above and read the difference before you optimise anything else.

Frequently asked

Use the data without scraping this page

Everything above is also published as machine-readable files. They are generated from this page's own model data during the build, which means a rate can never appear in one place and contradict the other. If you are writing an article, generating a model comparison, or feeding an LLM, use these instead of copy-pasting the table.

Rates carry a verification date and several are promotional. If you cite a number, cite the date with it — LLM pricing moves often enough that an undated figure is worth very little.

Sources & verification

Prices verified: November 2026 ·Sources: OpenAI Anthropic Google xAI

TokenMeter is not affiliated with OpenAI, Anthropic, Google, xAI or DeepSeek. Model names and prices are the property of their respective owners. This tool runs entirely in your browser — no request, token count or calculation leaves your device.

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