GPT 5.6 Terra
OpenAI's balanced GPT-5.6 tier — GPT-5.5-level quality at roughly half the cost, for production workloads.
OpenAI's GPT 5.6 Terra is a cloud text model. 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 Runware and Replicate using your own API key, from $2.00 per million input tokens.
- Released
- 1 month ago (Jul 2026)
- Pricing
- $2.00 / 1M in · $12.00 / 1M out
- Inputs
- Text, Images
- Context window
- 1.05M in · 128K out
- Reasoning
- Adjustable effort
- Controls
- System prompt
Examples
Generated with GPT 5.6 Terra via Replicate. 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 different software programs communicate. For example, a weather app can use an API to ask a weather service for today’s forecast, then display the answer to you.
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, authentication is rarely a differentiator, but it carries outsized security, compliance, reliability, password-reset, MFA, account-recovery, and abuse-prevention risk. A provider lets you launch faster, inherit mature security controls, support social login and enterprise requirements sooner, and keep your small team focused on the product customers actually buy. The strongest argument against this is strategic dependency: authentication sits at the center of your customer relationship, and a hosted provider can create vendor lock-in, rising costs, limited customization, outage exposure, and painful migration later. If your product’s core value depends on unusual identity rules, highly regulated data handling, or deeply customized authorization, building and owning the system may be worth the investment.
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 costs are driven by usage, technology choices, and a rapidly changing supplier market. Unlike traditional software licenses, many AI services charge for every interaction, document processed, image created, or model trained. A successful pilot can therefore become expensive quickly when adopted across the company, especially if employees use it frequently or feed it large volumes of data. Costs also vary with the quality and speed expected. A cheaper model may handle simple tasks, while customer-facing, regulated, or complex work may require more capable models that cost substantially more. Testing, monitoring, security controls, human review, and integration with existing systems can add costs that are not visible in an initial vendor quote. Third, AI technology and pricing are evolving quickly. Vendors change model capabilities, pricing tiers, limits, and contractual terms. New competitors may lower prices, but new features can encourage broader use. Demand can also be unpredictable: a marketing campaign, product launch, or automated workflow may create sudden spikes in activity. The company can improve predictability by treating AI as a managed utility. Set spending limits, usage alerts, approval thresholds, and department budgets. Start with measurable business cases, monitor cost per outcome, and review vendors regularly. Build reusable governance, data, security, and integration capabilities so each new use case does not start from scratch.
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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