Price claude: avoid common integration mistakes

Why your proxy key isn't working with the SDK

Your proxy key works because it translates standard OpenAI SDK calls to a different backend. The SDK sends a request to your base URL, and the proxy forwards it to the uncensored model server. If you see authentication errors, check that your API key is passed in the Authorization header as a Bearer token.

Some proxies require a specific header or prefix. Verify your client configuration matches the proxy's requirements. Use the official OpenAI SDK with a custom base_url to ensure compatibility.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.claudeprecio.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

• Check your base_url configuration.
• Ensure the API key is correct.
• Verify the model name matches the proxy's expected identifier.

Understanding input vs output token costs

Input tokens are the words you send, while output tokens are the words the model generates. Costs are calculated separately for each. If you send a 10,000-token prompt and receive a 1,000-token response, you pay for both.

Uncensored models often generate more verbose responses. This increases your output token count and your bill. Monitor your usage to avoid surprise charges.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.claudeprecio.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Set strict limits on output length to control costs. Use the max_tokens parameter to cap responses.

Context window limits and truncation errors

The context window includes both the prompt and the completion. If you exceed the limit, the model truncates the input or the output. This leads to incomplete responses or errors.

Our API supports a 100,000-token context window. However, the maximum output per request is 16,000 tokens. If you need longer outputs, implement chunking logic in your application.

Copycurl https://api.claudeprecio.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Calculate total cost for 10 million input tokens and 3 million output tokens"}]
  }'

• Calculate total tokens before sending.
• Truncate long prompts if necessary.
• Handle truncation errors gracefully.

Streaming vs non-streaming response handling

Streaming sends tokens as they are generated, allowing real-time display. Non-streaming waits for the full response before returning it. Streaming is useful for chat interfaces, while non-streaming is better for structured data.

Token counting differs between modes. In streaming, you receive tokens in chunks. Ensure your code aggregates these correctly to calculate costs accurately.

Copystream = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Tell the story in second person."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Use streaming for better user experience in chat applications. Implement proper error handling for dropped connections.

Function calling format compatibility issues

Function calling allows the model to return structured data. The format must match the SDK's expectations. If the proxy uses a different schema, your code may fail to parse the response.

Verify the function definition matches the SDK's version. Test with simple functions before scaling up. Use JSON mode for consistent output.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.claudeprecio.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

• Check function schema compatibility.
• Validate response structure.
• Handle parsing errors.

Rate limiting and concurrency limits explained

Rate limits restrict the number of requests per minute. Concurrency limits restrict simultaneous requests. Exceeding these limits results in 429 errors.

Our API allows 300 requests per minute and 8 concurrent requests per key. Implement exponential backoff to handle rate limit errors. Monitor your usage to stay within limits.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.claudeprecio.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Use a queue to manage concurrent requests. Implement retry logic for failed requests.

Crypto top-up timing and credit application

Crypto payments are processed on-chain. Transactions may take time to confirm. Credit is applied after confirmation. Use USDT on TRC20 or USDC on Base for top-ups.

Bonus credit is added for larger amounts. Ensure you send the correct amount to receive the bonus. Check the transaction status on the blockchain explorer.

Copycurl https://api.claudeprecio.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Calculate total cost for 10 million input tokens and 3 million output tokens"}]
  }'

• Send crypto to the provided address.
• Wait for confirmation.
• Verify credit balance.

Content refusal differences in uncensored models

Uncensored models do not refuse lawful adult content. They may still refuse illegal content, such as child sexual abuse material. This is a hard limit that always applies.

Verify the model's refusal policy before sending sensitive data. Use the model for creative writing or research. Avoid sending proprietary data if privacy is a concern.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.claudeprecio.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

Test with sample prompts to understand refusal behavior. Document the model's limits for your use case.

Best practices for production proxy usage

Monitor your token usage closely. Set alerts for budget limits. Use caching for repeated prompts to reduce costs.

Implement error handling for network issues. Use retries for transient failures. Test your application under load to ensure stability.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.claudeprecio.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

• Track token consumption.
• Implement caching.
• Monitor error rates.