GPT-5.4 Mini ($0.75/M) and Nano ($0.20/M) — Purpose-Built for Subagent Workloads

OpenAI shipped the first model family explicitly designed for subagent pipelines. GPT-5.4 mini features a 400K context window, scores 54.4% on SWE-Bench Pro (vs. the flagship's 57.7%), and handles computer use at 72.1% on OSWorld — at $0.75 input / $4.50 output per million tokens, 70% cheaper than base GPT-5.4. Cached input hits $0.075/M.

Nano is the real story for high-volume pipelines. At $0.20/M input and $1.25/M output, it's cheaper than Gemini 3.1 Flash-Lite and outperforms the previous GPT-5 mini at max reasoning effort. Simon Willison benchmarked vision tasks at 0.069 cents per image — 76,000 museum photo descriptions for $52.44 total.

The naming matters: "subagent workloads" is now a first-class product category. Every multi-agent pipeline has the same cost problem — running frontier models at every node burns budget on tasks that don't need frontier capability. Mini handles the thinking nodes; nano handles classification, extraction, and routing. The plan-and-execute pattern (Opus/GPT-5.4 as planner, mini/nano as executors) just got a 90% cost reduction on the executor side.

Mini is available in ChatGPT free tier via Thinking mode. Nano is API-only at launch. For teams already running heterogeneous model routing through tools like ccNexus or claude-launcher, these slot directly into the cheap-executor tier. For teams not yet doing model routing — this is the pricing signal that makes it irrational not to start. Source