OpenAI’s release of GPT‑6 Sol and Luna gives teams a way to get stronger performance without the high price tag of the top‑tier Astra model. Sol is built for complex coding and professional work, delivering scores that match or beat leading competitors while costing roughly half what the previous generation charged. Luna targets fast, high‑volume everyday tasks, offering the same quality as earlier models at a fraction of the price—its output token cost dropped from $1.20 to $0.50 per million tokens, a 58% reduction. Both models are API‑only, so there is no need to manage weights or host infrastructure; they are live today as gpt-6-sol and gpt-6-luna.
A common pain point is the repeated sending of identical instructions, tool definitions and conversation history in long‑running agents. GPT‑6 introduces an improved prompt caching system that can reuse shared prefixes within a 30‑minute window, cutting the cost of cached input reads by up to 90%. Developers can monitor hit rates via a dashboard, diagnose misses with a diagnostics tool, set explicit breakpoints, adjust reasoning effort mid‑conversation without breaking the cache, restrict callable tools with allowed_tools, and prewarm known context before the first request. These controls let teams keep the cache alive even when they need to change effort levels or tool availability, preserving savings.
Benchmark data shows Sol achieving 33.2% on AutomationBench at $0.27 per task—far cheaper than Claude Opus 5’s 26.9% at over ten times the cost. On DeepSWE v1.1 Sol scores 68.8% with about 80% lower cost per task than Claude Fable 5, and Luna matches Opus 5 and Fable 5 at medium effort while costing 93‑96% less. For computer‑use workflows, Sol’s xhigh setting reaches 60.5% on OSWorld 2.0 offline at roughly 80% lower cost than Opus 5 medium.
Choosing the right tier depends on workload: use Sol when you need coding depth or complex reasoning, Luna for bulk, low‑latency interactions, and enable prompt caching to maximize savings on any repetitive agent flow.
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