GPT-6 Luna review, pricing and verdict

GPT-6 Luna fits teams running high-volume chat, extraction, or classification work that doesn't need GPT-6 Astra's frontier reasoning, replacing GPT-5.6 Luna as OpenAI's cheapest GPT-6 tier. Its Artificial Analysis Coding Index of 41 trails Astra and even GPT-5.6 Luna's prior 43, so save it for lightweight agentic tasks, not frontier coding.

GPT-6 Luna is OpenAI's entry-level GPT-6 model, released September 22, 2026, with a context window of 1,050,000 tokens and a 37-point Artificial Analysis Intelligence Index score. It targets high-volume, latency-sensitive work like classification and summarization, and can handle lightweight agentic tasks at higher reasoning effort.

Provider: OpenAI · Family: GPT-6

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Context window: 1,050,000 tokens · Max output: 128,000

Input modalities: text, image · Output: text

About GPT-6 Luna

GPT-6 Luna, unveiled alongside an updated GPT-6 Sol on September 22, 2026, is the smallest and cheapest tier in OpenAI's GPT-6 lineup. Both arrived about three weeks after GPT-6 Astra, OpenAI's September 3, 2026 flagship, and OpenAI describes Sol and Luna as extending Astra's alignment work down to a faster, lower-cost tier. Luna sits below Sol in the lineup: Sol targets interactive and agentic coding, while Luna targets high-volume, latency-sensitive work like summarizing documents, extracting information and answering quick questions, with enough headroom at higher reasoning effort to take on lighter software-engineering and computer-use tasks that used to require Sol.

OpenAI has not published traditional academic scores (SWE-bench, GPQA, MMLU) for Luna specifically. Its own launch results, reported by Digital Applied on 22 September 2026, cover a set of agentic evaluations at max reasoning effort: 20.7% on AutomationBench, 50.9% on Agents' Last Exam, 42.4% on FrontierCode 1.1, 66.6% on DeepSWE 1.1, 52.7% on OSWorld 2.0 (offline), and a 7.6% factual error rate, down from GPT-5.6 Luna's 8.5%. Independently, Artificial Analysis's Intelligence Index scored GPT-6 Luna (max) at 37, level with GPT-5.6 Luna (max), while its Coding Agent Index came in at 41, two points below GPT-5.6 Luna's 43. Artificial Analysis also flagged regressions of roughly 75 Elo points on GDPval-AA v2.1 and 45 Elo points on AA-Briefcase v1.1 against the predecessor, tracing the drop to weaker presentation quality and deliverables that skip rubric elements.

The model has a 1,050,000-token context window (922,000 tokens of input headroom plus up to 128,000 output tokens) and a knowledge cutoff of May 18, 2026, per OpenAI's API documentation. Input modalities are text and image; output is text only, so Luna cannot natively read or generate audio or video despite the GPT-6 branding. Reasoning effort is adjustable across six levels, none, low, medium (the default), high, xhigh and max, and OpenAI's own results show most of Luna's capability gains appear only at the max setting.

OpenAI prices GPT-6 Luna well below GPT-5.6 Luna's earlier promotional rate; the exact per-token figures and cache discounts are broken out in the pricing FAQ below. The model reaches users through the OpenAI API (Responses and Chat Completions), the ChatGPT apps across every plan tier including Free and Go, Codex, and GitHub Copilot across VS Code, Visual Studio, the Copilot CLI, JetBrains, Xcode, Eclipse and github.com, with GitHub confirming its own rollout the same day as OpenAI's announcement.

Through the Responses API, Luna supports web and file search, image generation, a code interpreter, hosted shell access, apply-patch, skills, computer use, MCP and tool search, alongside function calling, structured outputs and streaming; the older Chat Completions endpoint only allows function calling when reasoning effort is set to none. OpenAI has not published a standalone system card for Luna: its September 22, 2026 revision to the GPT-6 Astra system card added information covering the other GPT-6 variants rather than issuing a separate document. Company-wide, API prompts and completions are excluded from OpenAI's model training by default, logs stick around for up to a month to monitor for abuse, and Zero Data Retention is available to eligible enterprise customers; it also holds SOC 2 Type II and ISO/IEC 27001 certification and can sign HIPAA business associate agreements.

Luna fits teams that need cheap, fast inference at scale: support-ticket classification, document extraction, chat, and lightweight agent loops where OpenAI's tool ecosystem matters more than top-tier reasoning. Teams running frontier coding, research or computer-use workloads are better served by GPT-6 Sol or GPT-6 Astra, since Luna's Coding Agent Index actually fell against its own predecessor. Teams needing audio or video I/O, or a wider context window, will need a different model entirely.

Pricing

Input costs $0.10 and output $0.50 per 1M tokens, cut roughly in half from GPT-5.6 Luna's prior promotional rate per Artificial Analysis (Sep 2026). Cached input is $0.01 per 1M (a 90% discount) and cache writes cost $0.125 per 1M (a 25% premium), per OpenAI's API docs. No separate batch discount is published for Luna as of 23 Sep 2026.

What a real job costs

Budgets: 20-page PDF = 30k in / 1k out · Support reply = 2k in / 300 out · Coding agent run = 200k in / 20k out. Computed from the vendor's per-token prices at render time; cached-input discounts are not applied.

Key Features

• Wide Context Window: Most of the token budget is reserved for input, with a separate, smaller allowance for output, per OpenAI's API documentation.
• Adjustable Reasoning Effort: Six effort levels, none through max, trade speed for accuracy; OpenAI's own agentic benchmark gains mostly appear only at max effort.
• Full OpenAI Tool Stack via the Responses API: Web and file search, code interpreter, hosted shell, computer use, MCP and tool search are all available, though Chat Completions only allows function calling at the none effort level.
• Prompt Caching: Cached input is billed at a steep discount versus a fresh prompt, OpenAI's standard mechanic for repeated prompt prefixes.
• Cross-Surface Availability: Ships alongside GPT-6 Sol in OpenAI's consumer, developer and coding-assistant surfaces on day one, from the chat app to IDE plugins.

Pros

• Cuts OpenAI's own per-token price roughly in half versus GPT-5.6 Luna's earlier promotional rate, per Artificial Analysis's September 2026 coverage.
• 157.2 output tokens/sec (Artificial Analysis) and a context window in the millions of tokens make it fast and roomy for a bottom-tier model.
• Ships identically across the API, ChatGPT's free and paid tiers, Codex and GitHub Copilot on day one, so teams already on OpenAI don't wait for platform support.

Cons

• Artificial Analysis's Coding Agent Index score fell slightly versus GPT-5.6 Luna (max), so it is not a pure across-the-board upgrade.
• Text and image only; there's no built-in way to process or produce audio or video, unlike some other 2026 frontier models in its price range.
• Function calling breaks on the older Chat Completions API unless reasoning effort is set to none, a trap for teams migrating existing integrations.

Benchmarks

• AA Coding Index: 41 cited: Artificial Analysis · 23 Sep 2026 — Composite of coding evaluations run by Artificial Analysis, 0 to 100.
• AA Intelligence Index: 37 cited: Artificial Analysis · 23 Sep 2026 — Composite of 10 evaluations run by Artificial Analysis, 0 to 100.
• AA blended price: $0.08/M cited: Artificial Analysis · 23 Sep 2026 — Price per 1M tokens at a 3:1 input to output blend, as listed by Artificial Analysis.
• Output speed: 157 tok/s cited: Artificial Analysis · 23 Sep 2026 — Median tokens written per second as measured by Artificial Analysis.

A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.

Visit GPT-6 Luna Official Page