Business data & reporting
Pull numbers from the systems you already use into one place an agent can analyze and explain.
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Concept
Stack 02 Stack 08
Tokens
Verified Oct 17, 2026 Review by Apr 15, 2027 5 sources
Short answer
Tokens are the chunks of text a model reads and writes. Context windows, output limits and API prices are all measured in them. Google says a Gemini token is about four characters, or 60 to 80 English words per 100 tokens. Prices are quoted per million tokens, and output usually costs several times more than input.
What it is
A token is a piece of text, often part of a word. Models don’t count words or pages. They count tokens. That makes tokens the unit for three things you care about: how much fits (the context window), how long a reply can be and what you pay.
How it works
Each vendor’s tokenizer splits text its own way, so the same document can be a different number of tokens on different models (checked 2026-10-17):
- Google: a Gemini token is about four characters, and 100 tokens is about 60 to 80 English words.
- Anthropic: Claude 4.7 and later models use a newer tokenizer that produces about 30% more tokens for the same text than earlier models.
API bills count input tokens (everything you send) and output tokens (everything generated). Depending on the vendor, output can include reasoning tokens.
Benefits
- A predictable cost unit. You can estimate a workflow’s cost from document sizes and run counts.
- Discounts. Vendors discount repeated input (prompt caching) and offline batch work.
Limits and risks
- Reasoning burns output tokens. Higher effort settings produce more tokens per answer.
- Not comparable across vendors. A “1M token” window isn’t the same number of words everywhere.
- Long-prompt price bands. Some models charge more above a prompt-size threshold. Google’s Gemini 3.1 Pro Preview, for example, lists a higher price for prompts over 200K tokens.
Checked specs
Snapshot from official sources on Oct 17, 2026.
| Tokens to words (Gemini) | ≈ 4 characters per token; 100 tokens ≈ 60–80 English words [3] |
| Anthropic newer tokenizer (Claude 4.7 and later) | ≈ 30% more tokens for the same text than the previous tokenizer [4] |
| Output vs input price, Claude Sonnet 5.5 | $10 vs $2 per 1M tokens [1] |
| Output vs input price, GPT-6.1 Sol | $10 vs $2 per 1M tokens [2] |
Best-fit stacks
Dev, web & IT
Design, code, host and ship sites and internal tools, with an AI editor doing most of the typing.
Agent compatibility
Not tool-specific. See Agent-ready for connector support by tool.
Levels come from each tool's recorded, sourced compatibility. How the levels work.
Related entries
Context window · ConceptHow do I cut AI API costs without hurting quality? · QuestionModel tiers (flagship, balanced, fast) · ConceptGrok models (xAI) · ModelInference · ConceptLarge language model (LLM) · ConceptPrompt caching · ConceptRate limits · ConceptReasoning models · ConceptShould I use RAG or put everything in the context window? · Question
Sources
- Models overview · Anthropicofficial accessed Oct 17, 2026
- Models · OpenAIofficial accessed Oct 17, 2026
- Understand and count tokens · Googleofficial accessed Oct 17, 2026
- Pricing · Anthropicofficial accessed Oct 17, 2026
- Gemini Developer API pricing · Googleofficial accessed Oct 17, 2026
Practitioner views are restated in our words and labeled as views, not measured results. Product facts come from official sources dated above.
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