LLM Token Counter & Cost Estimator

Count tokens for any text across 16+ models (Claude Opus 4.7, Sonnet 4.6, Haiku 4.5, GPT-4o, o3, Gemini 1.5, Llama 3, Mistral) and estimate per-call cost in USD. Claude via Anthropic SDK (BYO key, free endpoint), GPT via tiktoken (no key needed), others via heuristic. $0.001 per text counted.

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Why this exists

Every LLM engineer asks the same question 50 times a day: "if I send this prompt, how many tokens — and how much will it cost?"

Existing solutions:

• OpenAI tokenizer tool: web-only, OpenAI models only
• Anthropic count_tokens API: free but requires writing the SDK call
• Chargify / LLM-monitor SaaS: $50+/mo

This actor wraps all the major tokenizers and prices in one call. Drop a prompt in, get a sorted cost comparison across every model that could handle it.

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What you get

Summary row

{
  "_type": "summary",
  "char_count": 5230,
  "models_compared": 16,
  "cheapest_model": "gemini-1.5-flash",
  "cheapest_total_usd": 0.000405,
  "most_expensive_model": "claude-opus-4-5"
}

Per-model row (one per model)

{
  "_type": "model_count",
  "model": "claude-opus-4-7",
  "input_tokens": 1320,
  "chars": 5230,
  "method": "anthropic_count_tokens",
  "input_price_per_1m": 5.00,
  "output_price_per_1m": 25.00,
  "max_output_tokens": 1024,
  "input_cost_usd": 0.0066,
  "max_output_cost_usd": 0.0256,
  "total_cost_usd": 0.0322
}

Rows are sorted by input cost ascending — so the cheapest option is the first model row.

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Supported models

Model	Tokenizer	Pricing source
claude-opus-4-7	Anthropic count_tokens (or cl100k proxy)	$5 / $25 per 1M
claude-opus-4-6	same	$5 / $25
claude-sonnet-4-6	same	$3 / $15
claude-haiku-4-5	same	$1 / $5
gpt-4o	tiktoken o200k_base	$2.50 / $10
gpt-4o-mini	same	$0.15 / $0.60
gpt-4-turbo	cl100k_base	$10 / $30
gpt-3.5-turbo	cl100k_base	$0.50 / $1.50
o3	o200k_base	$15 / $60
gemini-1.5-pro	heuristic ~4 chars/token	$1.25 / $5
gemini-1.5-flash	heuristic	$0.075 / $0.30
llama-3-70b	heuristic	$0.59 / $0.79
mistral-large	heuristic	$2 / $6

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Quick start

Single text

{
  "text": "Hello, world. Today I am going to..."
}

From URL

{
  "textUrl": "https://en.wikipedia.org/wiki/Bitcoin"
}

Exact Claude counts (with Anthropic key — recommended)

{
  "text": "...",
  "anthropicApiKey": "sk-ant-..."
}

Compare only models you care about

{
  "text": "...",
  "models": ["claude-opus-4-7", "claude-sonnet-4-6", "gpt-4o", "gemini-1.5-pro"]
}

Realistic cost (set expected output length)

{
  "text": "...",
  "maxOutputTokens": 4000
}

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Use cases

1. Cost-sensitive prompt design — Try Opus 4.7 vs Sonnet 4.6 vs Haiku for the same prompt; pick cheapest that still works
2. RAG chunk sizing — Check if your retrieval chunks + system prompt fit in the context window
3. Batch budget forecasting — Run on 100 sample inputs → multiply for full corpus
4. Provider comparison — At what input length does Claude become cheaper than GPT? This actor answers in one call
5. Education — Show students why "translate this novel" hits the wall fast

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Pricing

Pay-Per-Event: $0.001 per text counted (regardless of how many models).

Vs Helicone ($25-100/mo for cost tracking), LangSmith, or building your own.

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How counting works per provider

• OpenAI (gpt-4o family) — exact via tiktoken library
• Claude — exact via Anthropic SDK count_tokens (free endpoint, requires anthropicApiKey). Without the key, falls back to cl100k tokenizer as a proxy (typically within ±5%).
• Gemini / Llama / Mistral — heuristic: ~4 chars per token. Accuracy varies by language. For Korean / Japanese / Chinese this overestimates by ~20%.

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Related actors (same author)

• Article Summarizer — uses Claude
• Web Structured Extractor
• PDF → RAG Chunks
• JSON Schema Generator

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Feedback

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