LLM Token Cost Calculator

QuantG Developer Tools

LLM Token & API Cost Estimator

Calculate token usage, compare pricing scaling across leading models, edit cost rates live, and download custom estimation reports.

Characters 0
Words 0
Est. Input Tokens 0
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Cost Scaling Comparison

Visual cost variance across provider architectures.

Model Pricing Matrix (Editable Rates)

Model Provider Category Input Rate ($/1M) Output Rate ($/1M) Single Execution
Edit input/output rates directly in the table to test custom pricing scenarios. Calculations use ~4 chars / 0.75 words per token heuristic.
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Optimize LLM Infrastructure

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Frequently Asked Questions

Understanding LLM Tokenisation & Cost Management

Everything you need to know about calculating model overheads, pricing anomalies, and managing production API bills.

For standard English prose, standard heuristics (~4 characters or roughly 0.75 words per token) give you roughly 90–95% accuracy compared to native BPE tokenisers like OpenAI's tiktoken. However, if your context includes complex JSON schemas, heavy code blocks, or non-Latin scripts (like Hindi or Arabic), token density spikes significantly. We recommend testing a sample batch in our analyzer before finalising your infrastructure budget.

Input processing is highly parallelised by cloud GPUs because the entire prompt is provided upfront. Output generation, on the other hand, is autoregressive—meaning the model must generate text sequentially, token by token, holding KV cache memory open throughout execution. This computes far higher hardware consumption on provider clusters, which is why completion tokens carry a 3x to 4x cost premium across almost all APIs.

Yes, absolutely. The model matrix above is fully interactive. You can modify both the Input and Output Rate ($/1M) fields directly within the table to reflect localized pricing, custom enterprise discounts, or newly updated rates. You can also add unlisted open-source or fine-tuned variants using the custom model card on the left.

If your monthly execution volume crosses 100M+ tokens or your workflow demands strict sub-500ms latency without data escaping your local server infrastructure, commercial API costs scale linearly into unsustainable figures. Transitioning to quantized open-weights models (like Llama 3.3 or DeepSeek) hosted on dedicated local inference pipelines typically drops long-term unit economics by 60–80%.

Retrieval-Augmented Generation (RAG) feeds large chunks of retrieved document text into the prompt window before answering a simple query. If vector chunking isn't optimized properly, you end up passing thousands of unnecessary tokens for every user interaction. Implementing semantic deduplication, hierarchical caching, or compact local rerankers prevents unnecessary context window inflation.