LLM Token Counter & Cost Estimator

About LLM Token Counter & Cost Estimator

The LLM Token Counter & Cost Estimator counts tokens for any prompt or document across the major commercial language models as of mid-2026 — the GPT-5 family (GPT-5 / mini / nano), GPT-4.1 family, GPT-4o, the o3 / o4-mini reasoning models, Claude Opus 4.7 / Sonnet 4.6 / Haiku 4.5, Gemini 3 Pro and the Gemini 2.5 family, xAI Grok 4 and Grok 4.1 Fast, DeepSeek V3 and R1, and Meta Llama 4 Scout / Maverick — and turns the count into a USD cost estimate using current per-1M-token pricing. OpenAI counts are exact via the official tiktoken encodings (o200k_base for GPT-5 / GPT-4o / o3 / o4-mini, cl100k_base for older models). For models whose tokenizers are not publicly available (Claude, Gemini, Grok, DeepSeek, Llama), counts are produced with cl100k_base and then corrected by a per-model calibration multiplier — yielding ±3-12% accuracy depending on the model. Every model badge shows its actual accuracy band so you know exactly how much to trust the number. Tokens are visualised in a colour-banded preview, context-window utilisation is shown as a gauge, and a slider for expected output length gives you the full round-trip cost.

Why use a LLM Token Counter & Cost Estimator?

Token counting is the universal currency of LLM development — it determines context-window fit, latency, and cost. Eyeballing 'this prompt is probably ~500 tokens' is a recipe for surprise overage bills and 'context too long' errors at runtime. This tool replaces the eyeball with an exact number for OpenAI models and a calibrated approximation for others, plus a USD cost figure you can paste straight into a budget proposal. The token visualisation panel is uniquely useful for prompt debugging — you can see at a glance that your variable name is 6 tokens, that a stray Unicode character cost you 8 bytes, or that a system prompt template is 30% larger than you thought.

Who is it for?

Built for AI engineers and prompt designers building production LLM apps, software developers integrating OpenAI / Anthropic / Google / Meta APIs, technical leads writing RAG and agent systems who need to size context windows correctly, finance / FP&A staff sizing AI budgets and unit economics, ML researchers comparing tokenizer efficiency across models, and educators or content creators teaching prompt engineering. Anyone who has ever opened a per-token API bill should bookmark this.

How to use the tool

Key Features

Common Use Cases

Frequently Asked Questions

Technical Specifications

Pro Tips

• Tokens, not characters, are the unit of cost — and the ratio is roughly 1 token ≈ 4 characters of English, but ranges from 1.5 (dense Unicode) to 8 (repeating whitespace). Always count, don't estimate.
• Variable names matter: `getUserById` is 4 tokens, `get_user_by_id` is 6, `getuser` is 2. In high-volume prompts, this adds up.
• JSON is token-expensive because of all the quotes and braces — every '{', '}', ',', and '"' is its own token. If you can switch to YAML or a custom DSL for system-prompt fixtures, you can drop 20-30% of system prompt tokens.
• Use Compare mode when migrating between providers — the same prompt can be 10-20% cheaper on a different model just from tokenizer differences, before any quality difference.
• If the context-window gauge goes red, trimming the system prompt is usually a much higher-leverage fix than truncating user input. System prompts are often 2-3× larger than they need to be.
• For RAG pipelines, run your retrieved chunks through this tool — chunks above 800 tokens are often retrieving more noise than signal and could be split.
• When you're optimising for cost, focus on reducing *output* tokens first (price 4-5× higher than input on most models). Stronger 'be concise' instructions, JSON schema constraints, and stop sequences are higher-leverage than prompt compression.
• Bookmark the URL — the tool restores nothing automatically (privacy), but having it one click away during prompt-engineering sessions saves real time.