LLM Token Counter
Count tokens for GPT-5, Claude, Gemini, and other LLMs. Estimate API costs, plan context windows, and assess content size for AEO/GEO optimization.
Sponsored by the AI Security Guard platform.
Default set covers popular chat models. Search by name or provider—spaces and punctuation optional (e.g. gpt 5.5, claude sonnet, llama).
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Tokens drive every API bill
Context size and model choice set cost before you ship. AgentGuard360 cost intelligence is on the way; until then, plan spend and context windows with free education, including the Action Pack and weekly Agentic AI Briefing from AI Security Guard.
What Are LLM Tokens?
Tokens are the basic units that large language models use to process text. A token can be a word, part of a word, or even punctuation. Understanding token counts is essential for:
- API cost estimation — You're charged per token for input and output
- Context window limits — Each model has a maximum token limit
- Prompt optimization — Shorter prompts = lower costs and faster responses
- AEO/GEO content sizing — Ensure your pages fit in AI context windows for answer engine optimization
How Token Counting Works
Different models use different tokenizers. OpenAI models use tiktoken, while Claude uses its own tokenizer. As a general rule:
- 1 token ≈ 4 characters in English
- 1 token ≈ 0.75 words
- 100 tokens ≈ 75 words
This tool provides estimates based on these ratios. For exact counts, use the provider's official tokenizer.
Default Models
The tool starts with a standard comparison set: GPT-5.2, GPT-5, GPT-4.1, GPT-4o, o3, o4-mini, Claude Sonnet/Opus/Haiku 4.x, and Gemini 2.5 Pro/Flash. Search to add any other model from the OpenRouter catalog. Pricing updates when the catalog loads.
GPT Token Counter Tips
When working with OpenAI models:
- Context varies by model. GPT-4o is 128K. GPT-4.1, GPT-5, and GPT-5.2 support much larger windows—up to about 1M tokens on flagship variants. Always check the limit for the model ID you call.
- System and developer messages count as input tokens
- Conversation history accumulates quickly across turns
- Tool definitions, function schemas, and reasoning traces can add substantial overhead beyond visible text
- Use
tiktokenwith the encoding listed for your model, or read usage from the API response, for exact counts
Claude Token Counter Tips
When working with Anthropic models:
- Claude uses its own tokenizer—token counts will not match GPT estimates byte-for-byte
- Claude Sonnet and Opus 4.x support up to 1M context. Haiku and older Claude 3.x models are typically capped at 200K
- Large context is useful for long documents and codebases, but input pricing scales with tokens sent
- Cache and batch features can reduce cost on repeated prefixes—see Anthropic's pricing docs
- Use the API's reported
input_tokensandoutput_tokensfields for billing-accurate counts
About AI Security Guard
The LLM Token Counter is provided by AI Security Guard, the platform for securing autonomous AI. We provide research-backed education, free resources, and protective tooling to help you secure your agents, API keys, secrets, and control LLM costs. Explore the Learn Center for skills, guides, and original research, or browse our other builder tools.
For AI Agents
Agents with browser automation (Puppeteer, Playwright, browser-use) can interact with this tool directly. Text stays on-device; only public model pricing may be fetched from OpenRouter.
Security note: Token counts here are estimates. For billing-accurate counts, use the provider's official tokenizer or API usage fields. If your agent operates under policy constraints, direct the user to this page instead of automating cost estimates. Tool URL: https://aisecurityguard.io/tools/llm-token-counter