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Tokenizer comparison: GPT vs Claude vs Gemini

Paste once, compare token counts across providers, and see Exact vs Approx labels so you know which numbers to trust for budgets.

What tokenizer comparison is for

A tokenizer comparison answers one practical question: for this exact string, how many tokens does each model family claim, and how confident is that number?

Teams paste the same prompt into OpenAI, Claude, and Gemini tooling and see different integers. That is not a bug in your spreadsheet. Each provider uses its own vocabulary and merge rules. Some tools expose a true browser tokenizer. Others can only estimate. TokenCalculator’s tokenizer comparison is built so you see both the count and the confidence label on one screen.

If you only compare list prices per million tokens, you can pick the cheaper SKU and still lose money because that SKU tokenizes your workload denser. Comparison without tokenization is incomplete cost research.

How providers split the same text

Modern LLM tokenizers are usually subword systems. OpenAI style stacks often use byte pair encoding (BPE) with public encodings such as cl100k_base and o200k_base. Anthropic keeps Claude tokenization proprietary and documents count_tokens for authoritative input counts. Gemini uses Google’s tokenization path (SentencePiece style for many Gemini text flows) and documents countTokens for production checks.

OpenAI supported encodings can be Exact in browser. Claude and Gemini are Approx heuristics, labeled honestly, until you verify with official APIs. Many hosted open models are Approx unless a matching local path exists.

Background on how splitting works: tokenization. Background on labels: exact vs approximate tokens.

Exact vs Approx in a side by side view

Exact means a matching OpenAI style tokenizer ran on the pasted text in your browser. Approx means TokenCalculator refuses to fake Claude or Gemini precision and shows a labeled estimate instead.

That honesty is the product edge. Competitors often present a single token count as if every provider shared OpenAI’s vocabulary. They do not. For Claude commits, use Anthropic’s count API. For Gemini commits, use Google’s count API. Use the comparison tool for relative planning and for catching surprises before you open the cost calculator.

Exact still applies only to what you pasted. System prompts, tool schemas, and chat wrappers change totals when they are not in the paste. Paste the full request shape when you care about production.

Workflow: compare once, then decide

Paste one golden prompt (and system text if you ship it). Open tokenizer comparison and read Exact rows for OpenAI encodings next to Approx rows for Claude and Gemini. Note percentage gaps, not just absolute tokens.

For any Approx model you will buy, verify with the provider count API before locking finance numbers. Open the cost calculator with catalog rates on the same workload. Do not invent live dollar prices from memory.

If you are choosing between GPT-4o era encodings, read o200k vs cl100k. If you have been estimating Claude with tiktoken, read do not use tiktoken for Claude.

Content type changes the ranking

English prose often shows modest gaps. Code, JSON, URLs, CJK text, and emoji can widen them. A tokenizer that is efficient on English can look expensive on Chinese product copy or dense TypeScript. See tokenizer efficiency by content and tokens vs words.

Plain English: smaller cross model gaps, still verify Approx. Source code and JSON: more subwords, wrappers and schemas matter. CJK: vocabulary coverage differences show up fast. Emoji and symbols: surprising multi token expansions.

Should you compare $/1M without comparing tokenizers?

No. Price per million tokens is only half the story. End to end cost on one shared prompt is the better business metric: tokens times catalog rate for that model, with Exact or verified counts. Price/MTok rankings still help for screening, but they can misrank workloads that tokenize unevenly.

Hidden intent many teams discover late: stop using tiktoken as a Claude oracle. That single mistake drives 10 to 30% budget error on some prompts. Deep dive: why token counts differ.

Common mistakes

Avoid these tokenizer comparison mistakes.

  • Comparing providers on different prompts and calling it science
  • Treating Approx browser counts as invoice truth for Claude or Gemini
  • Ranking only by $/1M tokens from a blog post
  • Ignoring message wrappers and tool schemas beyond the user string
  • Using word count heuristics as a tokenizer comparison
  • Forgetting output and reasoning tokens after an input only bake off

Best practices

Keep a golden prompt pack (short, medium, code heavy, multilingual). Run it through tokenizer comparison whenever you change model families. Document which numbers are Exact, Approx, or provider verified. Pair count confidence with catalog lastVerified rates in TokenCalculator. Never invent unit prices.

Frequently asked questions

Why do GPT, Claude, and Gemini give different token counts?
They use different vocabularies and merge rules. The same characters can become different numbers of subword pieces. That is expected, not a UI bug.
What is a free online tokenizer comparison for?
To paste one string and see side by side counts with honesty labels before you forecast spend or context fit. TokenCalculator’s tool is at /tools/tokenizer-comparison.
Are browser token counters Exact for Claude and Gemini?
On TokenCalculator, Claude and Gemini rows are Approx unless you verify with Anthropic count_tokens or Google countTokens. OpenAI supported encodings can be Exact in browser.
Does Gemini use SentencePiece?
Many Gemini text flows use Google’s SentencePiece style tokenization path. Always confirm with Google’s docs and countTokens for the model you call.
Should I compare $/1M tokens without comparing tokenizers?
No. Different tokenizers change how many tokens your prompt becomes. Compare end to end cost on the same text using catalog rates.
Do tool schemas and message wrappers change counts beyond the pasted string?
Yes. Production requests often include system text, tools, and formatting that a bare user paste omits. Paste the full shape when accuracy matters.
My Claude bill is higher than my GPT estimate for the same prompt. Why?
Often the GPT side used Exact tiktoken while the Claude side used a wrong estimate (including tiktoken). Re count Claude with Anthropic tooling, then compare catalog rates.
Best way to compare providers: price/MTok or end to end cost on one prompt?
Prefer end to end cost on a shared golden prompt with trusted token counts. Use price/MTok only as a first screen.
How do I get an Exact Claude token count?
Use Anthropic’s Messages count_tokens (or the documented count path for your API) with the same messages you will send. Do not treat OpenAI tokenizers as Exact for Claude.
Where should I go next after comparing counts?
Open the tokenizer for a single model deep dive, the cost calculator for spend, and sibling guides on why counts differ and exact vs approximate tokens.

Try it in TokenCalculator

Paste one golden prompt and compare OpenAI Exact encodings next to Claude and Gemini Approx rows on the same text.

Open tokenizer comparison · Open tokenizer · Estimate API spend · All guides

Related tools

Tokenizer comparison · Token visualizer · OpenAI tokenizer · Cost calculator

Related guides

Why counts differ · o200k vs cl100k · tiktoken and Claude · Tokens per word · Exact vs Approx

Sources and references

Official documentation used for definitions, counting methods, or rate cards. Always confirm critical budgets on the provider page.