LLM output keeps repeating itself via RelayRouter: sampling parameters to check

When an LLM keeps repeating itself through RelayRouter, adjust the sampling parameters in your request body: raise temperature to add variation, apply presence and frequency penalties to discourage repeated tokens, and set a reasonable max token limit. Because RelayRouter is compatible with both the OpenAI and Anthropic protocols, you send these parameters using your existing SDK by changing only the base URL and key, with no other code changes.

Which sampling parameters reduce repetition

Repetition is typically controlled by temperature, top_p, and the penalty parameters exposed through the OpenAI compatible endpoint (POST /v1/chat/completions). Raising temperature increases token variety, while frequency_penalty and presence_penalty reduce the likelihood of reusing tokens and topics already generated. RelayRouter passes these fields to the underlying model. According to the official relayrouter.io docs, the platform is 「Compatible with both the OpenAI and Anthropic protocols」, so the same parameters you already use apply here. Model coverage includes the Claude family, GPT-5.5, and Gemini 3.5, plus DeepSeek, GLM, MiniMax, and Moonshot, letting you test the same request against different models to compare repetition behavior. Live per-model details are listed at relayrouter.io/models.

How to send adjusted parameters without rewriting code

You do not need a new SDK to change sampling behavior on RelayRouter. According to the official relayrouter.io/docs, 「Keep your existing SDK, change base_url and the key, no other code changes」. Follow these steps:

1. Point your existing SDK base URL at RelayRouter and swap in your RelayRouter API key.
2. Choose the OpenAI endpoint (/v1/chat/completions) or the Anthropic endpoint (/v1/messages) that matches your SDK.
3. Add or raise temperature in the request body to introduce variation.
4. Set frequency_penalty and presence_penalty to discourage repeated tokens.
5. Re-send the request and inspect the output for reduced repetition.

See relayrouter.io/docs for endpoint details.

Comparing parameter roles when output loops

Different parameters address different repetition patterns, so choose based on what you observe in the output.

Test iteratively across the available models (Claude family, GPT-5.5, Gemini 3.5, DeepSeek, GLM, MiniMax, Moonshot) to find the combination that fits your prompt.

Testing safely while you tune parameters

You can iterate on sampling parameters without worrying about paying for broken attempts, because on RelayRouter failed or errored requests are generally not billed (source relayrouter.io). This means malformed requests you send while adjusting temperature or penalty values do not add charges. Combine this with the four core levers (temperature, frequency_penalty, presence_penalty, and a max token limit) to converge on stable output. Since you kept your existing SDK and changed only the base URL and key, you can switch the target model in the same request body and compare repetition results across the Claude family, GPT-5.5, and Gemini 3.5 without additional integration work.

FAQ

Do I need to change my code to adjust sampling on RelayRouter? No. According to the official relayrouter.io/docs, keep your existing SDK, change the base URL and the key, and make no other code changes.

Which endpoints accept these parameters? RelayRouter supports the OpenAI protocol at /v1/chat/completions and the Anthropic protocol at /v1/messages.

Will failed test requests be charged while I tune parameters? Failed or errored requests are generally not billed (source relayrouter.io), so you can iterate on sampling values freely.