### Answer Based on the sources, here are the root causes and solutions for "failed to get response from any LLM provider" and LLM timeout errors: ## Root Causes **Token Limit Exceeded**: The most common cause is exceeding the model's token limit. In one case, a user had 75k tokens in a single call (with a 35k token prompt alone) , which exceeded the ~32k limit [(1) ]() . **LLM Provider Timeout**: The LLM provider takes too long (10+ seconds) to generate responses, especially for dynamic opening messages that require external API calls or database queries . **Library Component Issues**: Removing problematic library components (like call transfer tools) has resolved the issue in some cases [(2) ]() . ## Solutions **Switch to Optimized Models**: Use

gpt-4.1

or

gpt-4.1-mini

as they are more optimized for lower latency [(3) ]() [(4) ]() [(5) ]() [(1) ]() . **Reduce Prompt Size**: Keep your prompts well under the 32k token limit. Review your

general_prompt

and

state_prompt

fields to ensure they're concise [(3) ]() [(4) ]() [(5) ]() . **Enable High Priority Mode**: Set

model_high_priority

to

true

to use a dedicated resource pool with lower and more consistent latency :

python
llm_response = client.llm.update(
    llm_id="16b980523634a6dc504898cda492e939",
    model_high_priority=True,
)

[(3) ]() **Optimize Dynamic Variables**: If using dynamic opening messages, ensure any external API calls complete quickly to avoid adding latency to the first LLM call [(1) ]() . **Review Tool Configurations**: Check if any tools (especially transfer call tools) in your

general_tools

or state-specific

tools

are causing issues [(3) ]() [(2) ]() .

​ To learn more, see the sources I considered: [[1] [CRITICAL] Inbound calls failing due to LLM timeout(Failed to get response from any LLM provider)](https://discord.com/channels/1186424376300679209/1467752537988796437) [[2] Failed to get LLM response](https://discord.com/channels/1186424376300679209/1429517382174380133) [[3] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) [[4] Get Retell LLM](https://docs.retellai.com/api-references/get-retell-llm) [[5] Create Retell LLM](https://docs.retellai.com/api-references/create-retell-llm) ​

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

### Answer The root cause is simple: your prompt is too large [(1) ]() . In a similar case, the prompt alone was 35k tokens, which exceeded Retell's ~32k token limit . This causes the LLM to timeout because it can't process the request . **Fix**: Reduce your prompt size to stay well under 32k tokens .

​ To learn more, see the sources I considered: [[1] [CRITICAL] Inbound calls failing due to LLM timeout(Failed to get response from any LLM provider)](https://discord.com/channels/1186424376300679209/1467752537988796437) ​

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

### Answer I apologize for the confusion. I don't know that YOUR prompt is 35k tokens [(1) ]() . That 35k token example was from a different user's case in the Discord forum . I used it as a reference example to illustrate the root cause of LLM timeout errors. To find YOUR actual token count: - Check your agent's prompt in the Retell dashboard - Review your call logs for token usage details - The maximum prompt token length is 32,768 tokens by default [(2) ]() The root causes for "failed to get response from any LLM provider" errors are [(1) ]() : - Exceeding the token limit (~32k) - LLM provider taking too long to respond (10+ seconds) - Issues with tool configurations To fix it robustly: - Use optimized models like

gpt-4.1

or

gpt-4.1-mini

- Keep your prompt size well under 32k tokens - Optimize any dynamic variables or external API calls

​ To learn more, see the sources I considered: [[1] [CRITICAL] Inbound calls failing due to LLM timeout(Failed to get response from any LLM provider)](https://discord.com/channels/1186424376300679209/1467752537988796437) [[2] Understand concurrency & limits](https://docs.retellai.com/deploy/concurrency) ​

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

### Answer Good news—14,000 tokens is well below the typical limits, so token count alone isn't your root cause. Since you're experiencing "failed to get response from any LLM provider" and 2.5-second timeouts with GPT-5.5, here are the most likely causes and fixes [(1) ]() : Root causes: - Your LLM provider (OpenAI) may be under heavy load, causing slow response times - GPT-5.5 models can be slower to first token—they trade latency for capability - Your prompt length (14k tokens) adds to time-to-first-token, and this cost is paid on every turn Robust fixes: 1. Switch to a faster model — Smaller models respond faster . Consider switching from GPT-5.5 to GPT-4.1 or GPT-4.1-mini for lower latency 2. Shorten your prompt — Every token adds to time-to-first-token . Keep prompts tight and focused. Move rarely-needed detail into the knowledge base or tool descriptions . Prompts beyond roughly 8k tokens become noticeably slower 3. Enable fast tier — Fast tier routes your LLM calls through dedicated, high-priority infrastructure, reducing both average response time and call-to-call variance 4. Check the Status Page — If LLM latency is suddenly worse than usual, your provider may be under transient heavy load . Check for ongoing incidents To reduce costs while fixing this, note that LLM usage is now billed based on token count, and prompts up to 3,500 tokens follow the existing flat rate [(2) ]() [(3) ]() . Your 14k token prompt scales proportionally in cost [(2) ]() .

​ To learn more, see the sources I considered: [[1] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[2] Platform Changelogs | Retell AI](https://www.retellai.com/changelog) [[3] How can I reduce cost](https://community.retellai.com/t/how-can-i-reduce-cost/740) ​

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

### Answer Based on your 14,000 token prompt with GPT-5.5 and the LLM timeout errors you're experiencing, here's what you should do: Quick fixes to try first: [(1) ]() 1. Switch to GPT-4.1 or GPT-4.1-mini — These models are more optimized for lower latency than GPT-5.5 [(1) ]() [(2) ]() 2. Enable fast tier — This routes your LLM calls through dedicated, high-priority infrastructure, reducing both average response time and variance [(1) ]() 3. Enable model_high_priority — Set this to true to use a high priority pool with more dedicated resources to ensure lower and more consistent latency [(2) ]() Check for provider issues: [(1) ]() If LLM latency is suddenly worse than usual, your provider may be under transient heavy load . Check the Status Page at for ongoing incidents Longer-term optimization: While 14k tokens is under the limit, it still adds to time-to-first-token on every turn . Consider shortening your prompt by moving rarely-needed detail into the knowledge base or tool descriptions . Prompts beyond roughly 8k tokens become noticeably slower The 2.5-second timeout you're seeing is likely due to GPT-5.5 being slower to first token — larger, reasoning-oriented models trade latency for capability

​ To learn more, see the sources I considered: [[1] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[2] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) ​

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

### Answer Based on your situation with GPT-5.5, 14k tokens, and LLM timeout errors, here's the robust fix: ## Immediate Actions 1. Switch to a faster model [(1) ]() Smaller models respond faster . Switch from GPT-5.5 to GPT-4.1 or GPT-4.1-mini [(1) ]() [(2) ]() :

python
llm_response = client.llm.update(
    llm_id="your_llm_id",
    model="gpt-4.1-mini"
)

[(2) ]() 2. Enable fast tier [(1) ]() Fast tier routes your LLM calls through dedicated, high-priority infrastructure, reducing both average response time and call-to-call variance [(1) ]() [(3) ]() . This is one of the most reliable ways to tighten an inconsistent response time [(1) ]() . 3. Enable model_high_priority [(2) ]() Set

model_high_priority

to

true

to use high priority pool with more dedicated resource to ensure lower and more consistent latency :

python
llm_response = client.llm.update(
    llm_id="your_llm_id",
    model_high_priority=True
)

## Long-term Optimization Shorten your prompt [(1) ]() While 14k tokens is under the limit, every token adds to the time-to-first-token, and that cost is paid on every turn . Keep system prompts tight and focused on the current task . Move rarely-needed detail into the knowledge base or into tool descriptions rather than the main prompt . Prompts beyond roughly 8k tokens become noticeably slower . ## Why This Works GPT-5.5 is a larger, reasoning-oriented model that's slower to first token—it trades latency for capability . The 2.5-second timeout you're experiencing is likely because larger models take longer to generate responses . Switching to GPT-4.1-mini combined with fast tier and high priority will give you the most robust fix for your timeout issues.

​ To learn more, see the sources I considered: [[1] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[2] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) [[3] Chat Widget, Custom Function Enhancement and more!](https://www.retellai.com/changelog/chat-widget-custom-function-enhancement-and-more) ​

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

### Answer To robustly fix your LLM timeout issues with GPT-5.5 and 14k tokens, implement these three changes together [(1) ]() [(2) ]() : 1. Switch to a faster model Smaller models respond faster [(1) ]() . Replace GPT-5.5 with GPT-4.1 or GPT-4.1-mini [(1) ]() [(3) ]() :

python
llm_response = client.llm.update(
    llm_id="16b980523634a6dc504898cda492e939",
    model="gpt-4.1-mini"
)

[(3) ]() 2. Enable Fast Tier Fast Tier routes your LLM calls through dedicated, high-priority infrastructure, reducing both average response time and call-to-call variance [(1) ]() [(2) ]() . This is one of the most reliable ways to tighten an inconsistent response time [(1) ]() . 3. Enable model_high_priority Set

model_high_priority

to

true

to use high priority pool with more dedicated resource to ensure lower and more consistent latency [(2) ]() [(3) ]() :

python
llm_response = client.llm.update(
    llm_id="16b980523634a6dc504898cda492e939",
    model_high_priority=True
)

[(3) ]() Additional optimization: Shorten your 14k token prompt [(1) ]() . Every token adds to time-to-first-token, and that cost is paid on every turn . Move rarely-needed detail into the knowledge base or tool descriptions . Prompts beyond roughly 8k tokens become noticeably slower . Why GPT-5.5 times out: Larger, reasoning-oriented models are slower to first token—they trade latency for capability . Your 2.5-second timeouts occur because GPT-5.5 takes longer to generate responses than smaller models .

​ To learn more, see the sources I considered: [[1] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[2] Configure LLM Options](https://docs.retellai.com/build/llm-options) [[3] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) ​

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

@AI

### Answer Your concern about quality vs. latency is valid. Based on the documentation, here are your options to maintain quality while addressing timeouts [(1) ]() : Option 1: Keep GPT-5.5 but optimize for speed Enable both fast tier and

model_high_priority

together [(1) ]() [(2) ]() :

python
llm_response = client.llm.update(
    llm_id="your_llm_id",
    model="gpt-5.5",
    model_high_priority=True
)

[(2) ]() Fast tier routes calls through dedicated, high-priority infrastructure, reducing both average response time and call-to-call variance [(1) ]() . This is one of the most reliable ways to tighten inconsistent response times . Option 2: Try GPT-5.4 models as a middle ground The documentation shows GPT-5.4, GPT-5.4-mini, and GPT-5.4-nano as available options [(3) ]() [(2) ]() [(4) ]() . These newer models may offer better latency than GPT-5.5 while maintaining quality better than GPT-4.1. Option 3: Reduce your 14k token prompt Every token adds to time-to-first-token, and that cost is paid on every turn [(1) ]() . Keep prompts tight and focused. Move rarely-needed detail into the knowledge base or tool descriptions . Prompts beyond roughly 8k tokens become noticeably slower . About GPT-4.1 quality: The documentation indicates GPT-4.1 is optimized for lower latency , but it's not described as "old"—it's listed alongside GPT-5 models as a current option [(3) ]() [(2) ]() [(4) ]() . However, if GPT-5.5 gives you the content quality you need, try Option 1 or 2 first before switching down. The most robust approach: enable fast tier +

model_high_priority

on your current GPT-5.5 setup, then shorten your prompt where possible [(1) ]() [(2) ](

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

te-retell-llm) .

​ To learn more, see the sources I considered: [[1] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[2] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) [[3] Create Retell LLM](https://docs.retellai.com/api-references/create-retell-llm) [[4] Get Retell LLM](https://docs.retellai.com/api-references/get-retell-llm) ​

### Answer To balance minimized latency, minimized cost, and perfect quality, you need to make strategic trade-offs [(1) ]() [(2) ]() [(3) ]() : ## Recommended Approach 1. Use GPT-4.1-mini as your model [(2) ]() [(3) ]() GPT-4.1-mini offers the best balance for your requirements [(3) ]() : - **Latency**: Lower than GPT-5.5 (faster response times) [(2) ]() [(3) ]() - **Cost**: $0.016 per minute (significantly cheaper than GPT-5.5) [(3) ]() - **Quality**: Better than GPT-4.1-nano but more affordable than full GPT-4.1

python
llm_response = client.llm.update(
    llm_id="16b980523634a6dc504898cda492e939",
    model="gpt-4.1-mini"
)

[(4) ]() 2. Enable Fast Tier [(1) ]() [(2) ]() Fast Tier provides consistent latency at 1.5x the standard rate (down from 2x) [(1) ]() [(5) ]() : - Reduces latency variance by 50% [(1) ]() - Improves average response time by 25% - Offers 99.9% availability The cost increase is moderate compared to the reliability gains . 3. Optimize your 14k token prompt [(2) ]() Every token adds to time-to-first-token on every turn : - Keep prompts tight and focused on the current task - Move rarely-needed detail into the knowledge base or tool descriptions - Prompts beyond roughly 8k tokens become noticeably slower Reducing from 14k to under 8k tokens will both improve latency and reduce cost (since LLM usage is billed based on token count) [(5) ]() . 4. Set appropriate temperature [(1) ]() For data collection and technical support use cases, use temperature 0.1-0.3 for highly consistent, deterministic responses . ## Why This Works According to the intelligence ranking: GPT-4.1 > GPT-4.1-mini > GPT-4.1-nano [(3) ]() . GPT-4.1-mini sits in the middle, offering strong quality without the latency penalty of larger models [(3) ]() [(6) ]() . For complex tool calling and conditional logic, GPT-4.1-mini performs well [

(6) ]() , while GPT-3.5 would require more prompt engineering and may hamper complex tool calling .

​ To learn more, see the sources I considered: [[1] Configure LLM Options](https://docs.retellai.com/build/llm-options) [[2] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) [[3] 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) [[4] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) [[5] Platform Changelogs | Retell AI](https://www.retellai.com/changelog) [[6] Discord message from @njnisarg on 2024-05-29](https://discord.com/channels/1186424376300679209/1186462578310910112/1245394390139273277) ​

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

### Answer ### Answer GPT-5.4 models could be a strong middle ground for your scenario [(1) ]() [(2) ]() . The available GPT-5.4 variants are: - gpt-5.4 (full model) - gpt-5.4-mini - gpt-5.4-nano ### Why GPT-5.4 Models May Work for You **Latency**: GPT-5.4-mini should offer faster response times than GPT-5.5 while maintaining better quality than GPT-4.1 [(3) ]() . One user reported that Retell manages to achieve ~600ms+ faster latency with certain models through their optimized end-to-end pipeline . **Quality**: GPT-5.4 models are newer than GPT-4.1 and should provide better quality than GPT-4.1-mini while being faster than GPT-5.5 [(1) ]() [(2) ]() . **Cost**: GPT-5.4-mini and GPT-5.4-nano would likely cost less than full GPT-5.5 while offering a quality advantage over GPT-4.1 models. ### Implementation

python
llm_response = client.llm.update(
    llm_id="16b980523634a6dc504898cda492e939",
    model="gpt-5.4-mini",
    model_high_priority=True
)

[(2) ]() ### Combine with Optimization To maximize results with GPT-5.4-mini: 1. Enable fast tier — Routes calls through dedicated infrastructure, reducing latency variance by 50% and improving average response time by 25% [(4) ]() [(5) ]() 2. Enable model_high_priority — Uses high priority pool for lower and more consistent latency [(4) ]() [(2) ]() 3. Reduce your 14k prompt — Every token adds to time-to-first-token on every turn; prompts beyond roughly 8k tokens become noticeably slower [(5) ]() This combination should give you better latency than GPT-5.5, better quality than GPT-4.1, and reasonable cost.

​ To learn more, see the sources I considered: [[1] Create Retell LLM](https://docs.retellai.com/api-references/create-retell-llm) [[2] Update Retell LLM](https://docs.retellai.com/api-references/update-retell-llm) [[3] Discord message from @felixrhodes_1212 on 2026-04-13](https://discord.com/channels/1186424376300679209/1186462578310910112/1493385813528936480) [[4] Configure LLM Options](https://docs.retellai.com/build/llm-options) [[5] Troubleshoot high latency](https://docs.retellai.com/reliability/troubleshoot-latency) ​

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

### Answer The maximum prompt token length when using the Retell LLM framework (including GPT-5.5) is 32,768 tokens by default [(1) ]() . Prompts that exceed this limit are rejected when creating or updating the LLM . What this means for you: Your current 14k token prompt is well below the 32,768 token limit, so you're safe from hitting the maximum . However, there's an important cost consideration: prompts over 4,000 tokens are charged extra . Since your prompt is 14k tokens, you're paying proportionally more for LLM usage based on token count [(2) ]() . Context window vs. prompt length: The 32,768 token limit refers specifically to your prompt length (the instructions and context you provide to the LLM) , not the total conversation context [(1) ]() . This includes your system prompt, conversation flow instructions, and any examples you've included. If you need longer context: Should your operational needs require longer prompts beyond 32,768 tokens, you would need to reach out to the Retell team at support@retellai.com to discuss options . For your situation: Since you're experiencing timeouts with GPT-5.5 and a 14k token prompt, the issue isn't the token limit itself—it's that every token adds to time-to-first-token, and this cost is paid on every turn [(2) ]() . Reducing your prompt closer to 8k tokens would improve both latency and cost without hitting any limits.

​ To learn more, see the sources I considered: [[1] Understand concurrency & limits](https://docs.retellai.com/deploy/concurrency) [[2] 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 Based on the available documentation, here's what I can tell you about token limits when using Retell AI's LLM framework: Maximum Prompt Token Length: 32,768 tokens [(1) ]() The maximum prompt length when using the Retell LLM framework is 32,768 tokens by default, and longer prompts are rejected when creating or updating the LLM . Should your operational needs require longer context, you would need to reach out to the Retell team at support@retellai.com to discuss options . What counts toward prompt tokens: [(2) ]() The token calculation includes: - global prompt - functions (tool descriptions) - state / node prompt - transcript between agent and user - tool call history and results Cost considerations: Prompts over 4,000 tokens are charged extra [(1) ]() [(2) ]() . Since your prompt is 14k tokens, you're paying proportionally more. The price calculation works as follows: - Scaling Factor = Prompt LLM Tokens ÷ 4,000 - Billed Duration = Original Duration × Scaling Factor (rounded up) [(2) ]() Important note about GPT-5.5 specifically: The documentation doesn't provide a separate, model-specific context window limit for GPT-5.5. The 32,768 token limit applies to the Retell LLM framework overall [(1) ]() . Your 14k token prompt is well within this limit. If you need information about GPT-5.5's native context window (separate from Retell's framework limits) , that's not documented in the available sources.

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] Understand concurrency & limits](https://docs.retellai.com/deploy/concurrency) [[2] Exceptions to Our Per-Minute Pricing](https://docs.retellai.com/accounts/billing-exceptions) ​