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AI Token Calculator

Paste your prompt and instantly compare token counts and costs across 15 AI models — including GPT-5.6, Claude 5, and Gemini 3.1.

Your Prompt / Text

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How token cost is calculated

1.Count tokens: Your text is split into tokens. ~4 characters = 1 token. "Hello world" ≈ 3 tokens. This is the same method used by OpenAI, Anthropic, and Google.
2.Input cost: You pay for every token in your prompt (what you send to the AI). Rate varies by model — cheaper models charge less per token.
3.Output cost: You also pay for every token the AI generates in its response. Output is usually 2–5× more expensive than input. The table below shows cost for your input only.
Cost = (your_tokens ÷ 1,000,000) × price_per_million_tokens

Cost Comparison — 15 Models

Sorted cheapest first

Input cost = cost to send your prompt. Output cost = cost per response of the same length. Real output length will vary.

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Paste your text above to see costs across all models

Which model should I use?

Price alone doesn't tell you which model fits your task. Use this as a starting point, then verify against the provider's own benchmarks for anything decision-critical.

ModelBest for
GPT-5.6 SolFrontier reasoning, native computer use, complex multi-step work
Claude Fable 5Anthropic’s top tier — long-context analysis, nuanced writing
Claude Opus 5Complex reasoning and code at a lower price than Fable 5
Gemini 3.1 ProLeads on GPQA Diamond and SWE-bench for its price tier — research, science, agentic coding
GPT-5.6 TerraBalanced quality and cost for everyday reasoning tasks
Claude Sonnet 5Balanced quality + cost — the model answering most day-to-day work
Gemini 3.6 FlashFast, multimodal, high-volume production traffic
DeepSeek V4 ProStrong reasoning and coding at a fraction of frontier pricing
Claude Haiku 4.5Fast responses for simple, high-volume tasks
Grok 4 FastLow-latency, low-cost tasks that don’t need frontier reasoning
Llama 4 ScoutOpen-weight option for self-hosting or fine-tuning
GPT-5.6 LunaOpenAI’s cheapest tier — high-throughput, budget-sensitive workloads
DeepSeek V4 FlashCheapest reasoning-capable option for simple tasks at scale
Amazon Nova LiteAWS-native, cost-effective for straightforward tasks
Amazon Nova MicroUltra-low cost — classification, extraction, simple lookups
Frontier benchmark snapshot (as of mid-2026): on GPQA Diamond, Gemini 3.1 Pro scores 94.3% and Claude Opus 4.7 scores 94.2% — both ahead of most models on hard science reasoning. On SWE-bench Verified, GPT-5.5 scores 88.7% and Claude Opus 4.7 scores 87.6% for real-world coding tasks. MMLU and HumanEval are excluded here since every frontier model now scores above 88% on both — they no longer distinguish between top-tier models.

Frequently asked questions

How is the token count calculated?

Using the standard approximation of ~4 characters per token, the same method OpenAI, Anthropic, and Google use for estimates. Actual tokenization varies slightly by model, but this gives a reliable estimate.

Is this an exact cost or an estimate?

It's an estimate. Input cost is calculated directly from your text. Output cost assumes a response of the same length as your input — actual response length varies by prompt and model.

Is my text sent to a server?

No. All token counting and cost calculation happens entirely in your browser using JavaScript. Nothing is sent anywhere.

How current is the pricing data?

Pricing reflects each provider's published rates as of August 2026. Model pricing changes frequently — verify against the provider's own pricing page before using these figures for budgeting decisions.

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