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Check here for common questions.

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1. Models and security

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Does tokensmind store my API request bodies?

  • No. tokensmind does not store request payloads or model responses. It only relays requests to providers and returns their responses.

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Why do Claude, GPT, Qwen “official apps” differ from API output?

The model is the same; official UIs add extra engineering (built-in prompts, tools, etc.).
  • Web apps are like a furnished apartment: search, memory, calculators, system prompts, etc.
  • API calls are like a shell: core capability only—you supply context and tools yourself.

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Why avoid GPT-5-class models in translation tools?

GPT-5 models target deep reasoning and structured output, not high-frequency realtime tasks.
  • Slower (more reasoning steps).
  • Higher token usage (system + reasoning context).
  • Translation plugins may hit safety filters more often.
For translation or chat, prefer GPT-4o mini or Gemini-class lighter models for speed and stability.

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Why does GPT-5 sometimes say “I am GPT-4” when asked who it is?

That is hallucination: wrong but confident claims about identity or capabilities. It happens across GPT-4, GPT-5, Claude, etc.
  • Not something we rewrite on the platform—it is normal LLM behavior.
  • “GPT-5” is a product name assigned after training; the weights are not “aware” of the label.
  • Web UIs can answer correctly via hidden system prompts; raw API calls may not.
  • Identity answers over the API can be inconsistent because the model has no true self-knowledge.

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Gemini-3-Pro often times out—what should I do?

Increase the client timeout. Large models can think for a long time; complex jobs may exceed 30s.
  • If you need Gemini-3-Pro, raise the timeout.
  • If you need speed, try lighter models (e.g. Gemini 2.0) with shorter timeouts.

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Why did one “hello” burn many tokens?

Some tools (Cline, Claude Code, etc.) attach hidden context or system prompts. That still counts as tokens. So even a short user message can include long history or instructions from the tool, not from tokensmind.

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What are the API concurrency limits?

There is no single global concurrency cap. If you hit concurrency issues, contact support.

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Why do identical prompts give different answers?

LLMs sample probabilistically (temperature, top-p, etc.), so token choices vary each run.
  • For steadier output, lower temperature or tighten sampling.
  • Context, system prompts, and network can also shift results.

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Claude replies cut off early—why?

tokensmind supports two styles for Claude:
  1. OpenAI Chat-compatible API
  2. Native Anthropic API
On the OpenAI-compatible path, max_tokens defaults to 4096. If you do not set a higher value, output stops at that cap—usually not a “bug,” just the limit.

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Longer answers

Set a higher max_tokens, e.g.: 
completion = client.chat.completions.create(
  model="claude-sonnet-4-6",
  max_tokens=6000,
  messages=[
    {
      "role": "assistant",
      "content": "Always reply in English"
    },
    {
      "role": "user",
      "content": "What is the meaning of life?, over 6000 words"
    }
  ]
)
Stay within the model’s maximum. If it still truncates, share the model name and full request for support.

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2. APIs and data

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Which endpoints exist?

Unified gateway, multiple conventions:
  • OpenAI-style: https://tokensmind.ai/v1 (GPT and compatible models)
  • Claude forwarding: https://tokensmind.ai (Anthropic SDK-style calls)

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What do you log?

We log what billing and operations need: account, call metadata, model id, token usage, payments.

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Privacy

  • We do not store user prompts or model outputs for content analysis or resale.
  • Underlying cloud or model vendors may keep their own access logs per their policies.

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3. Model behavior

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What is an AI hallucination?

Output that is false, unsupported, or fabricated.

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Causes can include

  • Biased or incomplete training data
  • Overfitting
  • Randomness in decoding
All major LLMs can hallucinate; it is not unique to one provider.

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4. Usage and troubleshooting

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How do I monitor usage?

Use the tokensmind console for volume, tokens, and billing. Filter by model and time window.

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When a call fails

Responses include codes and messages. Typical causes:
  • Malformed request
  • Model unavailable or over quota

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How should I manage API keys?

Create, revoke, and rotate keys in the console.

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Security tips

  • Never expose keys in public places
  • Separate keys per project
  • Rotate periodically