Price claude: avoid common token cost mistakes

Understanding input vs. output token pricing

The price of Claude API calls splits into two distinct parts. You pay less for reading text than for writing it. Input tokens cover the prompt you send. Output tokens cover the completion the model generates. This difference matters most when you send long code files but expect short answers.

Anthropic charges $3.00 per million input tokens for Sonnet 3.5. The same model costs $15.00 per million output tokens. That is a fivefold difference. You can save money by keeping your prompts tight. Do not send entire documentation dumps if only a few lines matter.

Our uncensored API flips this ratio slightly. We charge $0.15 per million input tokens and $0.50 per million output tokens. The gap remains, but the absolute cost is lower. You still pay more for generation. You still save by reducing input size.

• Input tokens are cheaper.
• Output tokens cost more.
• Balance both sides.

The hidden cost of large context windows

Large context windows consume memory even if you only read a small portion. Anthropic bills for the full context window used in each request. If you send a 100,000 token file, you pay for 100,000 tokens. You do not pay only for the lines the model reads. The entire window counts.

This creates a hidden cost for coding agents. They often send whole repositories. The model might only need one function. You still pay for the whole repo. Indexing helps. Send only relevant snippets. This reduces the input token count.

Our API supports a 100,000 token context window. You can send large files. You pay for what you send. Errors and refusals are free. This means you can test different snippets without burning credit. Use this to find the smallest effective prompt.

Why streaming affects token counting

Streaming does not change the total token count. It only affects how you see the numbers arrive. The model counts tokens the same way whether you stream or wait. The final bill is identical. Streaming just breaks the response into chunks.

Developers often confuse streaming with token savings. It is not. The API sends data in real-time. You see words appear as they are generated. The token counter updates at the end. You pay for the full completion. No tokens are saved by streaming.

Our API supports streaming via Server-Sent Events. You get token usage in the last chunk. This helps you track costs in real-time. You can stop early if the response gets too long. This saves output tokens. It does not save input tokens.

Common mistakes in tool-use token usage

Tool-use adds extra tokens to every request. You pay for the tool definitions. You pay for the arguments. You pay for the results. Each round of tool use costs more than a simple chat. Developers often forget to count these. They see a low input count. They miss the tool overhead.

Function calling requires a specific format. The model must parse the JSON. This adds complexity. It also adds tokens. You pay for the structure. You pay for the data. You pay for the result. Each step costs money.

Our API supports function calling. You can use tools. You pay for the tokens used. There are no hidden fees. You just pay for what you send. Keep your tool definitions short. Use concise argument names. This reduces the token count.

How to estimate costs before sending requests

You can estimate costs by counting tokens before sending. Use a tokenizer. Count the input tokens. Count the expected output tokens. Multiply by the price. This gives you a rough estimate. It is not exact. The model might generate more or fewer tokens. But it helps you budget.

Anthropic provides a cost calculator. You can input your token counts. It shows the total cost. You can compare different models. You can see the difference between input and output. This helps you choose the right model.

Our API uses standard pricing. $0.15 per million input tokens. $0.50 per million output tokens. You can use the same calculator. Just adjust the prices. This helps you compare our API with others. You can see the savings clearly.

Refunds and credit expiration policies

Credit never expires on our API. You pay once. You use it over time. There is no monthly fee. There is no subscription. You pay for what you use. This is different from Anthropic. They charge per request. You pay as you go.

If you make a mistake, you can request a refund. We fix double charges. We fix errors. The credit is yours. It does not disappear. You can use it whenever you want. This gives you flexibility. You do not need to rush.

Our trial credit expires in 7 days. This is the only time limit. You get $0.50. You can test the API. You can see how it works. You can decide if it is right for you. No phone number. Just sign up.

Comparing uncensored vs. standard model token efficiency

Uncensored models often generate longer responses. They do not refuse topics. They explain more. This can increase output tokens. You pay more for generation. But you get more content. This is a trade-off. You pay for detail. You get detail.

Standard models refuse more often. They give shorter answers. This saves output tokens. But you might need to ask again. You pay for the refusal. You pay for the re-prompt. The total cost might be higher.

Our uncensored API gives you full control. You can set temperature. You can set top-p. You can control the length. You can reduce output tokens. You can save money. You get the content you want.

Using seed and temperature to control output length

Temperature affects randomness. Higher temperature means more variety. Lower temperature means more consistency. You can control the output length. Set a low temperature. The model will be more predictable. It will generate fewer tokens.

Seed affects reproducibility. Same seed means same output. This helps you debug. You can get the same result. This is useful for testing. You can compare different prompts. You can see the difference.

Our API supports both. You can set seed. You can set temperature. You can control the output. You can save tokens. You can get the content you want. This gives you precision.