OpenAI is no longer asking teams to pay flagship prices for near-flagship capability. With GPT-6 Sol and GPT-6 Luna, the company is shipping two new tiers trained with the same methods as GPT-6 Astra, then cutting API prices in half relative to their GPT-5.6 predecessors.

The headline numbers are clean. GPT-6 Sol drops from $4 to $2 per million input tokens and from $20 to $10 for output. GPT-6 Luna goes from $0.20 to $0.10 on input and $1.20 to $0.50 on output. OpenAI credits caching and inference infrastructure improvements for letting the savings flow through.

The benchmark story is "near-flagship at a fraction of the cost." OpenAI picked five evaluations that map to complex professional work: AutomationBench, Agents' Last Exam, FrontierCode, DeepSWE 1.1, and OSWorld 2.0 offline.

On AutomationBench 1.0.6, which runs AI agents through end-to-end workflows across 47 tools spanning sales, marketing, operations, support, finance, and HR, GPT-6 Sol at xhigh effort scored 33.2% at $0.27 per task. Claude Opus 5 at max effort scored 26.9% at 11.1x Sol's per-task cost. Claude Fable 5.1 with Opus 5 fallback hit 31.4% at 8.9x. On Agents' Last Exam V1, GPT-6 Sol at max effort reached 56.4%, beating Claude Opus 5's top score while costing 60% less per task.

On the coding side, FrontierCode 1.1 Main grades coding agents on whether their changes can actually be merged into real codebases — not just correctness, but test quality, scope discipline, code style, and adherence to codebase standards. GPT-6 Sol improved substantially over GPT-5.6 Sol and matched Claude Fable 5.1 xhigh at much lower cost. On DeepSWE 1.1, GPT-6 Sol max effort scored 68.8%, within 1.1 percentage points of Claude Fable 5's 69.9% at xhigh, but at roughly 20% of the cost per task. GPT-6 Luna max effort scored 66.6%, comparable to Claude Opus 5 and Fable 5 at medium effort, while costing 93% less than Opus 5 and 96% less than Fable 5 per task.

On OSWorld 2.0 offline, GPT-6 Sol xhigh scored 60.5% — essentially matching Claude Opus 5 medium at 60.3% — at about 20% of the cost. GPT-6 Luna max beat GPT-5.6 Sol medium at one tenth the cost.

Caching as a product feature

The more interesting shift may be in the caching layer. OpenAI published a separate piece on the same day detailing prompt caching improvements for GPT-6: higher default cache hit rates, a 90% discount on cached input token reads, a Prompt Caching Dashboard, a diagnostics tool, and explicit breakpoint APIs that let developers adjust reasoning effort and tool availability without breaking cache. GitHub reported that across billions of requests to OpenAI models over the past few months, the share of prompt tokens requiring fresh processing fell by more than 50%, helping Copilot respond faster.

Alignment and availability

On alignment, OpenAI says Sol and Luna inherit the alignment work shipped with Astra, including lower rates of misleading claims about coding work in their internal coding deception evaluation.

Availability lands the same day in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu tiers, with Free and Go users getting Luna on desktop. On the API side, both ship as gpt-6-sol and gpt-6-luna, rolled out gradually throughout the day to keep service stable.

Why this matters

The philosophical move is more important than any single number: Astra is still the flagship, but OpenAI is now explicitly distributing "near-flagship capability at lower cost" as the design center of the GPT-6 family. Sol beats Astra at low effort on AutomationBench. Luna matches Opus 5 at medium effort on DeepSWE at 7% of the cost. The expensive-model-plus-high-effort combo is no longer the only way to clear complex workloads — and that resets the TCO math for anyone running Claude Opus or Fable in production today.

For enterprise buyers, the practical question is whether the cached-input economics, the new breakpoint APIs, and the 50% price cut combine into a workload they can actually migrate. For individual developers, Luna drops the entry price one more notch. Watch two things next: how visibly the style and factuality improvements show up for ChatGPT users (OpenAI claims "approaching Astra-level reliability" with "about half as many mistakes"), and whether Anthropic responds with pricing moves on the Opus 5 family.