LLM Cost Calculator & Model Comparison

Paste a prompt, see tokens and cost on GPT, Claude, Gemini, Qwen, DeepSeek — then compare two models

Your prompt or text

Expected reply length, tokens

Number of requests

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Words

≈ tokens (GPT)

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Input $ / 1M

Output $ / 1M

Context, K tokens

Model	Tokens	Input	Output	One request	All requests	Context	Fits?

Model A

Model B

Prices as of September 2026 (USD per 1M tokens).

Token counts are local estimates (each vendor's real tokenizer differs, typically by 5–15%; non-English text and code tokenize less efficiently). Prices are public list prices in USD per 1 million tokens, without caching or batch discounts, and change often: check the provider's pricing page before budgeting. Where a provider has price tiers only the standard one is shown (for example Gemini 3.1 Pro costs more above 200K tokens, DeepSeek shows peak-hour prices with half price off-peak, Gemini 3.8 Flash has a promotional price through 31 December 2026). Speed is a rough relative class, not a benchmark. Everything is calculated in your browser; your text is never sent anywhere.

About LLM pricing and context windows

Large language models are billed by the token, a small chunk of text roughly three-quarters of an English word. Providers charge separately for the tokens you send (input) and the tokens the model writes back (output), and output is usually several times more expensive, so a short question with a long answer can cost more than a long document with a short summary.

Price is only one axis when choosing a model. The context window sets how much text the model can consider at once, from around one hundred thousand tokens to a million or more; speed decides whether a chat feels instant; and multimodal models can also read images, listen to audio or watch video. Cheap fast models suit classification, extraction and high-volume chat, while flagship and reasoning models are worth their price on hard coding, analysis and planning tasks.

Comparing GPT, Claude, Gemini, Qwen, DeepSeek, Grok and Llama-class models side by side makes trade-offs visible before a project scales: multiply the per-request cost by your monthly volume and a tenfold price gap becomes a real budget line. Open-weight models add another option, since they can be run on your own hardware or through several competing hosts.