### Answer Retell AI offers the Retell LLM as their proprietary low-latency model, achieving an average end-to-end latency of 800ms [(1) ]() . This model is specifically fine-tuned for voice interactions with concise replies and reliable function calls, maintaining sub-800ms latency [(1) ]() [(2) ]() . For voice model selection, Retell AI provides several options with different latency characteristics [(3) ]() : - **Turbo v2.5**: Fast multilingual model - **Turbo v2**: Fast English-only model with pronunciation tag support - **Multilingual v2**: Rich emotional expression with a nice accent The Retell LLM achieves sub-600ms response times through optimized speech recognition pipeline, model inference optimization, streamlined text-to-speech synthesis, and edge deployment strategies [(2) ]() . For troubleshooting latency issues, if your LLM latency consistently exceeds 900ms, you can use the fast tier or switch to an alternative LLM provider [(4) ]() .

Feel free to tag @AI Evy with additional questions. I also appreciate feedback, it helps me improve.

​ To learn more, see the sources I considered: [[1] ](https://www.retellai.com/resources/ai-voice-agent-latency-face-off-2025) [[2] ](https://www.retellai.com/resources/retell-ai-receptionist-understands-calls-better) [[3] Platform Changelogs | Retell AI](https://www.retellai.com/changelog) [[4] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) ​

### Answer Based on the available sources, Retell AI's proprietary Retell LLM achieves sub-800ms end-to-end latency [(1) ]() , with the platform targeting sub-600ms response times through optimized speech recognition, model inference, text-to-speech synthesis, and edge deployment strategies [(2) ]() . However, the sources don't explicitly confirm whether 500ms end-to-end latency is achievable. The documented performance shows: - **Retell LLM average**: 800ms end-to-end latency [(1) ]() - **Target performance**: Sub-600ms [(2) ]() - **Normal LLM latency range**: 500ms - 900ms [(3) ]() End-to-end latency measures the total time from when the user stops speaking until the AI agent begins responding, including processing time, network delays, and model inference time [(4) ]() . While the sources indicate Retell AI achieves very low latency compared to competitors, I don't have explicit documentation confirming that 500ms end-to-end is currently achievable in production. The closest documented target is "sub-600ms" [(2) ]() , which suggests 500ms might be within reach under optimal conditions, but this isn't explicitly stated. For the most accurate answer about whether 500ms end-to-end is possible with your specific configuration, I'd recommend checking the [Status Page]() or reaching out to support with your speci

fic use case.

​ To learn more, see the sources I considered: [[1] Retell AI's Turn-Taking Model for Seamless Calls | Retell AI](https://www.retellai.com/blog/how-retell-ais-turn-taking-model-ensures-seamless-calls) [[2] ](https://www.retellai.com/resources/retell-ai-receptionist-understands-calls-better) [[3] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[4] Check actual latency](https://docs.retellai.com/reliability/check-actual-latency) ​

Feel free to tag @AI Evy with additional questions. I also appreciate feedback, it helps me improve.

### Answer Based on the available sources, achieving sub-500ms end-to-end latency would require optimizing multiple components in your voice AI stack. Here's what the documentation indicates: Latency Optimization Components [(1) ]() [(2) ]() To achieve the fastest possible latency, you need to optimize across several areas: **Speech Recognition Pipeline**: Use optimized automatic speech recognition (ASR) that begins processing audio streams before complete utterances finish [(1) ]() **Model Inference Optimization**: Implement specialized hardware acceleration and model quantization techniques that maintain accuracy while reducing computational overhead **Response Generation**: Use streamlined text-to-speech (TTS) synthesis that begins audio generation as soon as intent classification completes **Network Optimization**: Deploy edge strategies that minimize geographic latency between customers and processing infrastructure Specific Recommendations [(3) ]() [(4) ]() If your end-to-end latency exceeds targets: 1. Check features marked with a turtle icon 🐢 in your configuration and disable some to reduce latency [(3) ]() 2. Monitor your LLM response time - normal range is 500ms-900ms 3. If LLM latency consistently exceeds 900ms, use the fast tier or switch to an alternative LLM provider [(3) ]() [(4) ]() 4. Choose a lower-latency vo

ice provider if TTS is the bottleneck [(4) ]() 5. Optimize tool endpoints or reduce response size if tool calls are slow Geographic Considerations [(3) ]() International phone calls may introduce additional latency due to geographic distance. Consider getting a local phone number in the same region as your users and use phone numbers geographically closer to your target audience for better performance Benchmark Context [(2) ]() The sources show that Retell AI achieves approximately 780-800ms average latency in production benchmarks . While the platform targets sub-600ms response times through the optimizations mentioned above [(1) ]() , the documentation doesn't explicitly confirm that 500ms is consistently achievable across all configurations. For specific guidance on achieving sub-500ms latency with your particular setup, you may want to contact support with your call ID and configuration details [(3) ]() . **

​ To learn more, see the sources I considered: [[1] ](https://www.retellai.com/resources/retell-ai-receptionist-understands-calls-better) [[2] ](https://www.retellai.com/resources/sub-second-latency-voice-assistants-benchmarks) [[3] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[4] Address Metric Issues](https://docs.retellai.com/ai-qa/address-metric-issues) ​

Feel free to tag @AI Evy with additional questions. I also appreciate feedback, it helps me improve.

### Answer I don't have specific performance information about GPT-5.4-mini in the available sources. The sources confirm that

gpt-5.4-mini

is available as a model option in Retell AI's LLM configuration [(1) ]() [(2) ]() , listed alongside other models like

gpt-5.4

and

gpt-5.4-nano

. However, the documentation doesn't include specific performance metrics, latency benchmarks, or user feedback about how GPT-5.4-mini is performing compared to other models. For detailed performance information about GPT-5.4-mini, including latency characteristics, accuracy, or cost comparisons, you may want to check the [Status Page]() or contact support directly with your specific use case.

​ To learn more, see the sources I considered: [[1] Get Retell LLM](https://docs.retellai.com/api-references/get-retell-llm) [[2] List Retell LLMs](https://docs.retellai.com/api-references/list-retell-llms) ​

Feel free to tag @AI Evy with additional questions. I also appreciate feedback, it helps me improve.