Paste Text, Count Tokens, and Compare LLM API Cost Instantly
Paste a prompt, transcript, schema, or document chunk to see live token metrics first, then scan a compact cost comparison table for OpenAI, Claude, Gemini, and custom pricing.
Primary text-to-token workflow
Paste text here first. Token metrics update immediately, and the cost table below compares per-request and monthly cost across your selected models.
Current token cost snapshot
A quick read on what the current token count costs across the models you have selected.
Models to compare
Select the models you want in the comparison table
Cost breakdown
Review token counts and pricing by model, then export the scenario for planning, procurement, or customer quoting.
This local browser token counter keeps prompt text on your device. We only calculate tokens and pricing in the current session.
Using the built-in pricing fallback because the live catalog is unavailable right now.
OpenAI uses local tiktoken-compatible counting where available. Anthropic, Gemini, and custom models may use browser-side approximations, so always confirm final billing with provider dashboards for production budgets.
Advanced cost settings
Tune response size, cache assumptions, request volume, safety margin, and custom pricing without crowding the main paste-and-count flow.
Custom model pricing
How to estimate token pricing across LLM providers
Follow these steps to use the calculator as a cross-model token calculator, prompt and completion token calculator, and local browser token counter.
- Paste source text or enter manual token counts
Use text mode when you want local browser token counting from a prompt, schema, transcript, or RAG chunk. Use manual mode when you already know the token count from another pipeline.
- Choose providers and scenario assumptions
Select OpenAI, Claude, Gemini, or a custom model, then fill in expected output tokens, cached input tokens, additional retrieval tokens, request volume, and monthly usage.
- Model batch, caching, and margin effects
Turn on batch discount for async bulk jobs, add cached prompt tokens for repeated system instructions, and include a safety margin or client markup if you need budget guardrails.
- Compare and export the result
Review per-request cost, scenario total, monthly cost, safe budget, and markup-adjusted price. Export JSON or CSV for procurement reviews, customer quotes, or model-selection docs.
Case studies: where token cost estimation matters
These examples match high-intent search behavior around pricing comparison, caching, batching, and multilingual LLM usage.
Case Study 1: Agentic workflow cost estimator
Profile
A startup running multi-step agents with planner, retriever, and reviewer loops.
Challenge
The team needed to estimate how repeated tool calls and long system prompts would affect unit economics before launch.
Solution
They used the calculator to model prompt tokens, completion tokens, extra retrieval context, and batch discounts across candidate models.
Implementation
Each agent step was pasted into text mode, then the team adjusted monthly requests and safety margin until the scenario matched their production forecast.
Results
They identified the cheapest model mix for the workflow and cut projected monthly cost by more than a third before shipping.
Case Study 2: OpenAI vs Claude API pricing calculator
Profile
A support platform comparing GPT-4o-mini with Claude 3.5 Sonnet for chat handling.
Challenge
They needed a fast way to compare prompt and completion token pricing on the same conversation history without writing custom scripts.
Solution
The calculator processed a representative chat transcript locally and returned side-by-side monthly estimates for both providers.
Implementation
The team pasted several 10-turn conversations, set projected request volume, and compared the markup-adjusted price for enterprise plans.
Results
They selected the lower-cost option for standard support cases and reserved the premium model for escalation paths only.
Case Study 3: Batch API cost calculator
Profile
An operations team processing tens of thousands of product descriptions overnight.
Challenge
Their margin depended on whether async batch pricing materially changed the cost of large content-refresh jobs.
Solution
They modeled the job with batch discount enabled and included a buffer for long-tail descriptions that ran larger than average.
Implementation
The team entered a representative sample, projected total request count, and exported the CSV for budget approval.
Results
They moved the workload to the batch queue with a clear savings estimate and gained a predictable overnight processing budget.
Case Study 4: Anthropic context caching cost
Profile
A legal-tech workflow with large reusable system prompts and policy documents.
Challenge
The team needed to understand how much cached prefixes would reduce the cost of repeated queries over the same base instructions.
Solution
They used cached input tokens to model repeated context and compared the effective monthly savings against uncached operation.
Implementation
The shared legal instructions were entered as cached tokens, while dynamic matter-specific prompts and outputs were estimated separately.
Results
They justified prompt caching internally and reduced the apparent cost of high-compliance workflows.
Case Study 5: Multilingual LLM token cost
Profile
A global content team localizing prompts and structured outputs across English, Japanese, and Chinese.
Challenge
Word counts looked similar, but token usage varied sharply by language and output format.
Solution
They pasted localized prompts into the tool to measure token inflation and compare provider pricing before launching in new markets.
Implementation
The team duplicated scenarios by language, adjusted expected output size, and documented the price delta by market.
Results
They prevented underpricing in high-token languages and set market-specific usage policies with better confidence.
Token cost estimator FAQs
What is a cross-model token calculator?
It is a tool that lets you estimate token usage and API cost across multiple LLM providers from the same input so you can compare pricing before you build.
How accurate is this llm token cost estimator?
OpenAI-compatible models use local tokenizer support where available. Other providers can rely on browser-side approximations, so the estimate is strong for planning but provider billing dashboards remain the final source of truth.
Why separate prompt and completion tokens?
Most providers charge different prices for input and output tokens, and output is often much more expensive. Splitting them makes the estimate usable for real budgeting.
Can I estimate anthropic context caching cost here?
Yes. Add the portion of your prompt that is reused as cached input tokens, then compare the scenario against uncached runs to see how repeated prefixes change the budget.
Does this work as a batch api cost calculator?
Yes. Enable the batch discount toggle to estimate the lower total you would expect from async bulk processing workflows.
Is my data stored when I use this local browser token counter?
No. The calculator is designed for local execution in the browser session, so pasted prompts and documents stay on your device during estimation.
Can I use this as a rag chunk token estimator?
Yes. Paste a representative document chunk, then add extra input tokens for retrieval overhead and multiply requests to model the cost of top-K retrieval patterns.
How do I estimate openai structured output token pricing?
Paste the prompt and any schema or structured-output instructions into text mode, then set the expected completion tokens so you can see how formatting overhead changes total cost.
Why does multilingual llm token cost vary by language?
Different tokenizers split non-English text differently, so similar word counts can produce very different token totals. Testing each target language is the safest way to price global usage.
Can I compare a custom or self-hosted model?
Yes. Use the custom pricing section to enter your own model name and per-million token rates for input, output, and cached input.