### Answer Here are some best practices to reduce latency for your Retell AI voice agents: Monitor and Identify Latency Sources - Check your agent's estimated latency in the agent detail page under "Estimated Latency" [(1) ]() - Monitor actual call latency in the "Call History" section, which shows P90, Median, and Min latency metrics [(2) ]() Avoid Settings That Increase Latency - Certain settings will increase latency—these are marked with a turtle icon in the dashboard [(1) ]() Architecture Optimizations - Use edge deployment to reduce geographic latency [(3) ]() - Implement streaming optimization with chunked audio processing to minimize buffering delays - Use predictive pre-loading for context anticipation to reduce response preparation time - Consider adaptive bitrate for dynamic quality adjustment under network stress LLM and Voice Selection - Choose faster TTS models like ElevenLabs Flash v2.5 or Flash v2 for "blazing fast latency" [(4) ]() - Consider using OpenAI's Fast Tier for predictable latency and higher reliability General Targets - Production voice AI agents typically aim for 800ms or lower latency to maintain conversational flow [(3) ]() - Retell AI achieves latency as low as 600ms [(1) ]()

​ To learn more, see the sources I considered: [[1] Check estimated latency](https://docs.retellai.com/reliability/check-estimated-latency) [[2] Check actual latency](https://docs.retellai.com/reliability/check-actual-latency) [[3] ](https://www.retellai.com/resources/ai-voice-agent-latency-face-off-2025) [[4] Platform Changelogs | Retell AI](https://www.retellai.com/changelog) ​

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

### Answer --- The "best" LLM depends on your priorities—latency, accuracy, or cost. Here's a quick breakdown: Available Options in Retell - GPT-3.5-turbo and GPT-4-turbo are available through the API and dashboard [(1) ]() Key Trade-offs | Model | Strength | Consideration | |-------|----------|---------------| | GPT-4o | Higher accuracy (88.7 MMLU score) , multimodal support, real-time audio via Realtime API [(2) ]() | Higher cost ($0.0025/1K input tokens) | | GPT-4o Mini | Much cheaper ($0.00015/1K input tokens) | Lower capability for complex tasks | | Claude 3.5 Sonnet | Excels at nuanced instructions, safety-focused | Slightly slower (2-4 seconds response) | | Claude 3 Haiku | Lower cost ($0.012/min) | Less capable than Sonnet | For Low Latency Since you're focused on reducing latency, prioritize models with faster inference. Your response system's time-to-first-sentence latency directly impacts overall experience. [(3) ]() Custom LLM Option You can also integrate your own LLM (no charge for the LLM portion) if you need specific optimizations. [(1) ]() For a deeper dive, check out [Choosing the Best LLM for Your Voice AI Agents]() and [LLM Best Practices]() . [(3) ]() [(2) ]()

​ To learn more, see the sources I considered: [[1] New LLM Model Options, Modular Pricing, And More](https://www.retellai.com/changelog/new-llm-model-options-modular-pricing-and-more) [[2] Choosing the Best LLM for Your AI Voice Agents | Retell AI](https://www.retellai.com/blog/choosing-the-best-llm-for-your-voice-ai-agents) [[3] Integrate LLM](https://docs.retellai.com/integrate-llm/integrate-llm) ​

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

### Answer If latency is your primary concern and cost is not a constraint, here are the key recommendations: Use Fast Tier Enable Fast Tier, which routes your LLM calls through dedicated, high-priority infrastructure for superior performance and consistency. [(1) ]() Key benefits include: - Consistent latency with predictable response times for every call - Higher availability with priority access to compute resources - 50% reduction in latency variance - 25% improvement in average response time Fast Tier pricing is 1.5x the standard rate, but since cost isn't a concern for you, this is ideal. LLM Selection For latency-sensitive use cases, GPT-4.1 has been suggested as a faster option. [(2) ]() Additional Settings - Use a lower temperature setting (0.0–0.3) for function calling and data collection tasks, as this produces more deterministic responses. [(1) ]() - Enable Structured Output for more reliable function calling, though note it may have slightly slower auto-save times. Retell AI's platform achieves sub-600ms response times through optimized speech recognition, model inference, and TTS synthesis. [(2) ]()

​ To learn more, see the sources I considered: [[1] Configure LLM Options](https://docs.retellai.com/build/llm-options) [[2] Latency Issues](https://discord.com/channels/1186424376300679209/1446234465675378739) ​

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

### Answer I don't have a specific latency ranking of LLMs in the available sources. What I can share: - The LLM you choose affects how fast your voice agent responds—different models vary in latency, context retention, and specialized vocabulary handling. [(1) ]() - Retell AI's platform achieves sub-600ms response times through optimized speech recognition, model inference, and TTS synthesis. [(2) ]() - Enabling Fast Tier routes LLM calls through dedicated, high-priority infrastructure, delivering a 50% reduction in latency variance and 25% improvement in average response time. [(3) ]() For a detailed comparison of LLM options and their trade-offs, check out: - [Choosing the Best LLM for Your Voice AI Agents]() - [LLM Best Practices]() Would you like me to try a general answer based on my knowledge instead?

​ To learn more, see the sources I considered: [[1] 2025 B2B Guide to AI Phone Calls & Voice Agents | Retell AI | Retell AI](https://www.retellai.com/blog/b2b-guide-to-ai-phone-calls) [[2] ](https://www.retellai.com/resources/retell-ai-receptionist-understands-calls-better) [[3] Configure LLM Options](https://docs.retellai.com/build/llm-options) ​

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

### Answer ### Answer Here's a latency comparison based on available data: *OpenAI GPT Models (Latency: Lower is Faster) * [(1) ]() | Model | Latency | |-------|---------| | GPT-4.1 Nano | ~500ms (lowest) | | GPT-4.1 Mini | ~550ms | | GPT-4o Mini | ~650ms | | GPT-5 Nano (minimal) | ~700ms | | GPT-4.1 | ~720ms | | GPT-4o | ~750ms | | GPT-5 Mini (minimal) | ~800ms | | GPT-5 (minimal) | ~1000ms (highest) | Anthropic Claude Models [(2) ]() Claude models prioritize safety over speed, often taking 2–4 seconds for responses. This makes them effective for high-compliance scenarios but slower for real-time voice agent needs. Google Gemini The sources don't contain specific latency benchmarks for Gemini models on Retell AI. Summary for Latency-Focused Use Cases If latency is your priority: [(1) ]() - GPT-4.1 Nano or GPT-4.1 Mini offer the lowest latency - GPT-5 Mini provides a balance of improved reasoning with acceptable latency (~800ms) - Claude is notably slower (2–4 seconds) and better suited for compliance-heavy applications rather than real-time voice [(2) ]() Community feedback also suggests GPT-4.1 has "excellent latency" in live testing, while Claude 3.7 Sonnet can have "egregious lag" spikes. [(6) ]()

​ To learn more, see the sources I considered: [[1] Retell AI Launches GPT-5 for Voice Agents – Higher Intelligence, Smarter Conversations | Retell AI](https://www.retellai.com/blog/gpt-5-now-on-retell-smarter-ai-voice-agents-with-reasoning-power) [[2] Choosing the Best LLM for Your AI Voice Agents | Retell AI](https://www.retellai.com/blog/choosing-the-best-llm-for-your-voice-ai-agents) ​

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