How to pick the right LLM for a production chatbot

The best LLM for a production chatbot is almost never the highest-quality model. For interactive use cases, you need to optimize across three axes simultaneously: response quality, inference latency, and per-token cost. The right choice depends on your traffic volume, your tolerance for slower replies, and how much quality you can trade away before users notice.

Why latency matters more than you think

Users in a chat interface expect responses to begin within ~500ms and stream at a pace that feels natural. That means output speed is a hard constraint, not a nice-to-have. A model generating 43 tok/s will feel sluggish on long answers; one generating 237 tok/s will feel instant.

GPT-5.4 Mini (OpenAI) is the standout here: 237 tok/s output speed, a quality index of 48.1, and $1.69/M tokens. Compare that to Claude Opus 4.6 (Adaptive Reasoning) (Anthropic), which scores 53.0 on quality but crawls at 51 tok/s and costs $10.00/M tokens. That's a 4.6x speed difference and a 5.9x cost difference for 5 points of quality. In most chatbot scenarios, users won't perceive that quality gap, but they will perceive the latency gap.

The cost math at scale

A chatbot serving 1M conversations/day at ~1,000 tokens per conversation burns through 1B tokens/day. At that scale, the difference between $0.52/M and $5.63/M is the difference between $520/day and $5,630/day. That's $1.86M/year in additional spend.

Here are the models worth considering for high-volume production chatbots: