which is the best llm that i can use for my agent ...
# ask-air
rhythmic-breakfast-40081
05/07/2026, 11:24 PMwhich is the best llm that i can use for my agent in terms of accuracy and cost
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tall-airline-16756
05/07/2026, 11:24 PMThanks for reaching out, Anurag. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
tall-airline-16756
05/07/2026, 11:24 PMThanks — you're asking which LLM is best for an agent when balancing accuracy and cost. A few clear options depending on your priorities:
• If accuracy is top priority (cost secondary): GPT‑4o — highest accuracy and low latency, but higher cost.
• If you want strong accuracy with low cost: GPT‑4.1 mini or Gemini 2.5 Flash — both give “great” accuracy at much lower per‑minute and per‑token prices.
• If you want the best balance (accuracy vs cost): GPT‑4.1 mini — solid accuracy with very low cost and good latency.
If you tell me your expected usage pattern (tokens/minute or minutes/month), latency needs, and whether safety/guardrails or multimodal capabilities matter, I can calculate rough monthly cost estimates and recommend the single best model for your agent.
Sources: Pricing | LiveKit | Build voice, video, and physical AI | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit, with interactive elements
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rhythmic-breakfast-40081
05/07/2026, 11:27 PMcurrently i am using gpt 4.1 as main llm and gemini 2.5 flash as fall back i am having 8 intents in my project and the llm has to decide the intent and then execute the tools and my system depends on the system prompt. but the cost is higher and the token usage as well and so i am even trying for a lower latency
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tall-airline-16756
05/07/2026, 11:27 PMThanks — I hear you: you’re using GPT‑4.1 as primary and Gemini 2.5 Flash as fallback for an 8‑intent agent, and you want lower cost, lower token use, and better latency while keeping intent accuracy and tool execution reliable.
A few quick clarifying questions so I can give targeted recommendations:
• What’s your monthly usage (approx.): average requests/day, average tokens/request, or minutes/month?
• Typical latency target (e.g., <200ms, <500ms)?
• How often does the model need to run full system prompts vs. short prompts (are system prompts included every call)?
• How critical is perfect intent accuracy vs. acceptable occasional misclassification? (Can you tolerate a small drop for big cost savings?)
• Are tool calls multi‑turn (context must be preserved) or single‑turn per intent?
• Do you have any hard constraints (on‑prem, data residency, or model provider restrictions)?
Based on your answers I can:
• Recommend specific model(s) and fallback strategy (e.g., smaller fast model for classification + larger model for tool execution),
• Show prompt/system‑prompt strategies to cut tokens (few‑shot moves to cache, use concise system prompt, or only send system prompt when intent uncertain),
• Suggest batching, caching, or classifier+LM hybrid patterns with rough cost/latency tradeoffs and an estimated monthly cost., with interactive elements