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🎟️ AI Token Counter & Cost Comparator

Paste any prompt and instantly see how many tokens it uses on every major LLM β€” GPT-5, Claude Opus 4.8, Gemini 2.0, Llama 3.3, DeepSeek R1 and 35+ others β€” plus the exact input/output price per call and per month.

Pricing verified 2026-07-05
Your prompt / context 0 chars Β· 0 words
Presets:
⚠ Non-Latin script detected. Chinese, Japanese, Korean and Arabic text typically use 2–3Γ— more tokens per character. Costs shown may under-estimate real usage.
Providers: OpenAI Anthropic Google Meta Mistral DeepSeek xAI Cohere Alibaba
πŸ“Š Monthly projection (requests/day Γ— 30 days)
Model Input tokens Output tokens Input $ Output $ Per call Per month Context
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About AI Token Counter

AI Token Counter is a free browser-based tool that tells you how many tokens your prompt uses on every major LLM and how much each one costs to run. Paste your text and get an instant side-by-side comparison across OpenAI (GPT-5, GPT-4o, o1, o3), Anthropic (Claude Opus 4.8, Sonnet 5, Haiku 4.5), Google (Gemini 2.0), Meta (Llama 3.3), Mistral, DeepSeek, xAI Grok, Cohere and Alibaba Qwen β€” 40+ models in total.

Costs are calculated per call and projected to monthly totals based on your traffic. Advanced knobs cover prompt caching, batch-API discounts, vision surcharges, reasoning-model overhead, and non-Latin-script warnings β€” everything you need to budget a real LLM deployment before shipping.

100% client-side: your prompt is tokenized and priced in your browser. Nothing is uploaded β€” safe for confidential system prompts, proprietary context, and private data.

How to use AI Token Counter

1

Paste your prompt

Include system message + user turn + any retrieved context. Everything the model sees counts as input.

2

Set expected output

Pick "same as input", "empty" for classifiers, or a fixed length. This drives the output-cost side.

3

Enter your volume

Requests per day β†’ monthly projection. Turn on cache or batch if you're using them.

4

Compare + export

The table sorts cheapest-first. Copy the report as Markdown or download as CSV/JSON.

Key Features

40+ models across 9 providers

OpenAI (GPT-5, GPT-4.5, GPT-4o, GPT-4o mini, o1, o1-mini, o3, o3-mini, GPT-4 Turbo, GPT-3.5), Anthropic (Opus 4.8, Opus 4.7, Sonnet 5, Haiku 4.5, 3.5 Sonnet, 3.5 Haiku, 3 Opus/Sonnet/Haiku), Google (Gemini 2.0 Pro/Flash, 1.5 Pro/Flash/Flash-8B), Meta Llama 3.3/3.1, Mistral, DeepSeek V3 & R1, xAI Grok, Cohere Command R+, Alibaba Qwen.

Per-family tokenizer heuristics

GPT-family uses OpenAI's cl100k_base / o200k_base heuristic. Claude uses ~3.5 chars/token. Gemini ~3.9. Llama ~3.8. Mistral ~3.7. DeepSeek ~3.4. Every non-OpenAI count is flagged with a ~ so you know it's an estimate.

Input + output cost separation

Output tokens cost 3–5Γ— more than input on most providers. We split them so you can budget accurately β€” especially for chat apps where output length dominates.

Prompt caching + batch discounts

Toggle prompt caching to see Anthropic's 90% cache-read discount (or OpenAI's 50% cached-input rate). Batch API cuts OpenAI + Anthropic by 50% for async jobs β€” huge for offline pipelines.

Reasoning-model multiplier

o1, o3, DeepSeek R1 emit hidden "thinking tokens" that are billed. Our 1×–10Γ— slider adds that overhead so your projection isn't 3Γ— too low.

Currency-aware

Toggle USD / EUR / GBP / INR / JPY. Baked-in FX rates convert every number in the table.

Volume + monthly projection

Enter requests/day and see monthly totals for every model. Perfect for capacity planning or a pre-launch cost model.

Context-window warnings

If your input + output exceeds a model's context window, the row highlights red with the overflow. Never ship a prompt that a model will reject.

Non-Latin script alert

CJK and Arabic text use 2–3Γ— more tokens per character. When we detect them, we surface a warning so your estimate isn't wildly optimistic.

Export as Markdown / CSV / JSON

Copy the comparison as a Markdown table for docs or Slack, or download raw data for spreadsheets and cost dashboards.

Common Use Cases

  • Choosing an LLM before you ship β€” cheapest model that fits your context
  • Budgeting a chatbot: 10,000 users Γ— 20 turns/day β†’ what does it cost?
  • Comparing GPT-4o vs Claude Sonnet 5 for a specific prompt template
  • Sizing your RAG chunks β€” is 5K tokens per query affordable at your scale?
  • Evaluating switch to Haiku 4.5 or Gemini 1.5 Flash to slash costs
  • Estimating fine-tuning ROI vs base model + long prompts
  • Pre-computing batch pipeline cost for overnight jobs
  • Sanity-checking a vendor's billing invoice
  • Teaching / writing about tokenization and pricing tiers
  • Confirming your prompt fits in a small-context model before switching

Security & Privacy

  • 100% client-side: tokenizing + pricing runs in your browser via plain JavaScript. Your prompt never leaves your device.
  • Works offline: once the page loads, disconnect and it still works.
  • Safe for confidential prompts: system messages, proprietary context, API keys, PII β€” none of it is uploaded.
  • No logging: we don't record what you paste, how many tokens, or which model you compared.
  • Pricing accuracy: we manually verify per-provider pricing and stamp a "verified" date. Always double-check with the vendor before contracting to a budget β€” prices change quarterly.
  • Tokenizer accuracy: only OpenAI's tokenizer is publicly documented. Claude, Gemini, Llama and others use approximations tuned to be within ~5% for English text and code.

Frequently Asked Questions

For OpenAI models (GPT-4o, GPT-4, o1) we use a tuned cl100k_base/o200k_base heuristic that's typically within 3–5% of exact tiktoken counts on English prose and code. For Claude, Gemini, Llama, Mistral etc. the counts are family-specific character-ratio approximations β€” accurate enough for budgeting but not exact. Non-Latin scripts (Chinese, Japanese, Arabic) can be off by more; we surface a warning when detected.
Output tokens are 3–5Γ— more expensive on almost every provider because each output token requires a full forward pass through the model β€” much more compute than reading input. Claude Opus 4.8 is $15/1M in vs $75/1M out (5Γ—). Design prompts to minimize verbose responses when you can β€” structured JSON output, explicit length limits, and "concise" instructions all cut real dollars.
Anthropic and OpenAI both let you mark a prefix (system prompt, RAG context, few-shot examples) as cacheable. Re-reading a cached chunk costs 10–50% of the normal input price. If your prompt has a big static prefix reused across many calls, turn on the toggle and watch costs plummet. Cache writes cost 25% extra one-time, so it only pays off after ~2 reads of the same prefix.
OpenAI and Anthropic both offer a Batch API that runs jobs asynchronously (up to 24 hours) at 50% off. Perfect for offline pipelines β€” data labeling, backfills, embedding generation, overnight report processing. Not usable for interactive chat.
o1, o3 and DeepSeek R1 do internal chain-of-thought before answering. These "reasoning tokens" are billed as output but you never see them β€” a simple prompt can secretly emit 5,000 tokens of hidden thinking. Set the multiplier to reflect how heavy your prompts are: 1Γ— for classifiers, 3Γ— for typical questions, 10Γ— for complex proofs.
Yes β€” set "Images attached" > 0 and we add a per-image token surcharge to vision-capable models (GPT-4o β‰ˆ 1,105 tokens/image at high detail, Claude β‰ˆ 1,500, Gemini β‰ˆ 258). Actual counts depend on image resolution and "detail" mode.
We manually verify all provider pricing and stamp a "verified" date at the top of the page. Prices are checked at least monthly and any time a new model launches. Always double-check with the provider's official pricing page before committing to a contract.
No. Everything runs in your browser. Disconnect from the internet after the page loads β€” the tool still works. Safe for confidential system prompts, proprietary context, and PII.
Roughly: Haiku 4.5 or Gemini Flash for high-volume simple tasks; Sonnet 5 or GPT-4o for the sweet spot of quality + price; Opus 4.8 or o3 for the hardest reasoning. DeepSeek V3 and Llama 3.3 are the cheapest capable general-purpose models. Always A/B on your actual prompts β€” quality can matter more than 10Γ— price differences.