Token Counter
Count tokens across GPT-5, Claude 4, Gemini 2.5, Llama 4 with live cost estimation
| Model | Input tokens | Input $ | Output $ | Total $ | Context fit |
|---|---|---|---|---|---|
GPT-5 OpenAI · 272,000 ctx | 96 | $0.000120 | $0.005000 | $0.005120 | ✓ fits (0.2%) |
GPT-5 mini OpenAI · 272,000 ctx | 96 | $0.000024 | $0.001000 | $0.001024 | ✓ fits (0.2%) |
GPT-5 nano OpenAI · 272,000 ctx | 96 | $0.000005 | $0.000200 | $0.000205 | ✓ fits (0.2%) |
GPT-4.1 OpenAI · 1,047,576 ctx | 96 | $0.000192 | $0.004000 | $0.004192 | ✓ fits (0.1%) |
o3 (reasoning) OpenAI · 200,000 ctx | 96 | $0.000192 | $0.004000 | $0.004192 | ✓ fits (0.3%) |
Claude Opus 4.7 Anthropic · 1,000,000 ctx | 101 | $0.001515 | $0.037500 | $0.039015 | ✓ fits (0.1%) |
Claude Sonnet 4.6 Anthropic · 1,000,000 ctx | 101 | $0.000303 | $0.007500 | $0.007803 | ✓ fits (0.1%) |
Claude Haiku 4.5 Anthropic · 200,000 ctx | 101 | $0.000101 | $0.002500 | $0.002601 | ✓ fits (0.3%) |
Gemini 2.5 Pro Google · 2,097,152 ctx | 107 | $0.000134 | $0.005000 | $0.005134 | ✓ fits (0.0%) |
Gemini 2.5 Flash Google · 1,048,576 ctx | 107 | $0.000032 | $0.001250 | $0.001282 | ✓ fits (0.1%) |
Gemini 2.5 Flash-Lite Google · 1,000,000 ctx | 107 | $0.000011 | $0.000200 | $0.000211 | ✓ fits (0.1%) |
Llama 4 Maverick Meta · 10,000,000 ctx | 98 | $0.000026 | $0.000425 | $0.000451 | ✓ fits (0.0%) |
Llama 4 Scout Meta · 10,000,000 ctx | 98 | $0.000011 | $0.000170 | $0.000181 | ✓ fits (0.0%) |
DeepSeek V3.1 DeepSeek · 128,000 ctx | 98 | $0.000026 | $0.000550 | $0.000576 | ✓ fits (0.5%) |
DeepSeek R1 (reasoning) DeepSeek · 64,000 ctx | 98 | $0.000054 | $0.001095 | $0.001149 | ✓ fits (0.9%) |
Mistral Large 2 Mistral · 128,000 ctx | 120 | $0.000240 | $0.003000 | $0.003240 | ✓ fits (0.5%) |
Mistral Medium 3 Mistral · 128,000 ctx | 120 | $0.000048 | $0.001000 | $0.001048 | ✓ fits (0.5%) |
Grok 4 xAI · 256,000 ctx | 96 | $0.000288 | $0.007500 | $0.007788 | ✓ fits (0.2%) |
How to use
- Paste your prompt text
- Set expected completion length
- Review per-model tokens + cost + fit
Use cases
- LLM cost estimation
- Prompt engineering
- Context-window budgeting
About the Token Counter
Every call to an LLM API bills per token — the atomic unit each model breaks your text into. Knowing the token count before you call is essential for cost forecasting, context-window budgeting, and avoiding mid-response cutoffs. This tool estimates tokens across every frontier model's tokenizer and multiplies by current API prices so you can compare apples to apples.
Features
- Estimates for 17+ frontier models across 7 tokenizers
- Per-model $ cost for input + configurable output length
- Context-window fit indicator with utilization %
- Handles Unicode + CJK with model-aware factors
- Live per-keystroke updates
How it works
- Paste or type your prompt text.
- Set expected completion length (for total-cost estimation).
- Review per-model input tokens, per-call cost, and context-window fit.
Frequently asked questions
Is this exact?
+
Estimates are calibrated from publicly reported tokenizer characteristics (~3.6–4.0 chars/token English). English text is typically ±3% vs. official tokenizers. For exact GPT counts use OpenAI's tokenizer; for Claude use Anthropic's token-count API.
Why does the same text give different counts across models?
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Each model family ships with its own tokenizer (GPT uses tiktoken, Claude uses its own ~100k vocab, Gemini uses SentencePiece). More specialized tokenizers ≠ fewer tokens — it depends on how well the vocab matches your text.
How do I lower my token count?
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Remove redundant whitespace, use concise phrasing, put long static context behind prompt-caching, and prefer models with larger English-optimized vocabularies (Claude, GPT-4o) for long English prose.
Which model is cheapest per million tokens?
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Varies by workload — see our LLM Price Comparison tool. As of April 2026: Llama 4 Scout, Gemini Flash-Lite, and GPT-5 nano are the budget leaders.
Does the count include my system prompt?
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Yes — paste everything you'll send (system + user + any prior assistant turns) to get a realistic estimate.