OpenAI GPT 6 Luna Pro
GPT 6 Luna in OpenAI's pro reasoning mode, which works longer on each answer for higher quality on hard tasks.
GPT 6 Luna Pro is a cloud text model built by OpenAI. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. Its context window handles up to 1.05M input tokens and up to 128K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions. It runs through OpenRouter using your own API key, from $0.10 per million input tokens.
- Released
- this month (Sep 2026)
- Pricing
- $0.10 / 1M in · $0.50 / 1M out
- Inputs
- Text, Images
- Context window
- 1.05M in · 128K out
- Reasoning
- Adjustable effort
- Controls
- System prompt
Examples
Generated with GPT 6 Luna Pro via OpenRouter. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is a set of rules that lets one software program request information or actions from another. It works like a clearly defined doorway: a program asks for something in an expected way, and the other program responds without revealing how it works inside.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
Use a hosted authentication provider. For a two-person startup, secure password handling, account recovery, MFA, and ongoing security maintenance consume time better spent building the product, and a reputable provider is likely to handle them more reliably. The strongest argument against this is dependence on a third party: an outage, pricing change, or forced migration could disrupt sign-ins and leave you with costly work at the worst possible time.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
AI spending is hard to predict because it depends on more than the number of employees. A single request can trigger a chain of model calls, searches, data retrieval, and retries. Usage also fluctuates with demand: seasonal peaks or a popular feature can multiply activity. Second, prices and capabilities change rapidly. Providers revise rates, introduce new models, or change which tasks require expensive reasoning and longer responses. A system that is economical in a pilot may cost much more at full scale. Third, costs are distributed across vendors and teams. Cloud infrastructure, model access, software subscriptions, integration work, security, and human review may appear on separate budgets, hiding the true total. Poorly managed experiments can duplicate tools or retain unused capacity. Executives can improve predictability by treating AI as a metered utility, not a fixed license. Set owners and budgets for each use case, monitor requests and cost per completed outcome, and establish alerts and spending limits. Start with small pilots, then forecast using realistic demand scenarios before expansion. Compare providers and models regularly, routing simple tasks to less costly options. Review results monthly and stop projects that do not deliver measurable value. Preserve flexibility because both prices and usage will shift unpredictably over time.
OpenAI
OpenAI builds GPT, DALL·E, the Sora family, and the open-weight gpt-oss models, and has been a central force behind the modern wave of generative AI.
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