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How many tokens is your prompt?
Paste any prompt or text to estimate its token count, compare the input cost across GPT-4o, Claude, Gemini, DeepSeek and more, and check whether it fits each model’s context window. Nothing is sent anywhere.
Input cost & context fit
| Model | Provider | Input cost | Context | Fits? |
|---|---|---|---|---|
| GPT-4o | OpenAI | $2.0e-4 | 128K | ✓ fits |
| GPT-4o mini | OpenAI | $1.2e-5 | 128K | ✓ fits |
| Claude Sonnet 4.5 | Anthropic | $2.4e-4 | 200K | ✓ fits |
| Claude Opus 4 | Anthropic | $1.2e-3 | 200K | ✓ fits |
| Gemini 2.5 Pro | $9.9e-5 | 1M | ✓ fits | |
| Gemini 2.0 Flash | $7.9e-6 | 1M | ✓ fits | |
| DeepSeek V3 | DeepSeek | $2.1e-5 | 128K | ✓ fits |
| Mistral Large | Mistral | $1.6e-4 | 128K | ✓ fits |
Token counts are an estimate — each model's tokenizer differs, and code, JSON, and non-English text tokenize differently. Costs are approximate public input prices per 1M tokens. VernaOne losslessly compacts large JSON inputs (typically 28–49%) before they reach the model.
Paying for tokens you don’t need?
VernaOne losslessly compacts large JSON inputs before they reach the model — typically 28–49% fewer input tokens on data-heavy prompts — and routes to the cheapest capable model with automatic fallback.
Try VernaOne free →Frequently asked
How many tokens is my prompt?
Paste your text into the counter above for an instant estimate. As a rule of thumb, one token is roughly 4 characters or about 0.75 words of English, so ~1,000 words is ~1,300 tokens. Code, JSON, and non-English text tokenize differently, so treat the number as an estimate — exact counts depend on each model’s tokenizer.
How much will a prompt cost?
The tool multiplies your estimated input tokens by each model’s input price, so you can compare the cost of the same prompt across GPT-4o, Claude, Gemini, DeepSeek and others. Add your output tokens and monthly volume in the LLM Cost Calculator for a full monthly estimate.
Will my prompt fit in the context window?
The counter shows each model’s context window and whether your text fits. If you’re close to the limit, either trim/compress the input or use a long-context model like Gemini (up to ~1M tokens) or Claude (~200K).
How do I reduce tokens without losing information?
Cut boilerplate, trim restated input, and compress structured data. VernaOne losslessly compacts large JSON inputs into a columnar form before they reach the model — typically 28–49% fewer input tokens on data-heavy prompts — with no data loss.