OpenAI GPT-OSS 20B
OpenAI's open-weight 20B reasoning model (Apache 2.0) with configurable reasoning effort and native tool calling — runs on 16 GB.
From OpenAI, GPT-OSS 20B is an open-weight text model. It takes a text prompt and replies with generated text. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Aug 2025
- Pricing
- Free, runs on your hardware
- Text rank
- #270
Samples
Generated with GPT-OSS 20B via Ollama. 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, or Application Programming Interface, is like a set of rules that lets one computer program talk to another. It specifies the exact commands, inputs, and outputs, allowing software components to interact without needing to understand each other's inner workings.
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.
For a two‑person startup the smart move is to use a hosted authentication provider such as Auth0, Firebase Auth, or Amazon Cognito. These services give you instant, secure, GDPR‑compliant login, multi‑factor, single sign‑on, and social integration without the heavy engineering burden. They scale automatically and keep you out of the trenches of cryptographic detail, letting you focus on core product features. The single strongest counter‑argument is that you give up full control of user data and become subject to the provider’s pricing tiers and data residency constraints; if the service changes its policy or pricing, or if you need a highly customized flow that the provider can’t accommodate, you’ll be forced to migrate or pay premium fees.
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.
Predicting the cost of artificial‑intelligence projects is notoriously difficult for business. One major reason is the rapid evolution of underlying technologies; algorithms that were state‑of‑the‑art two years ago can become obsolete almost overnight, forcing companies to update hardware, software licences and talent, with no clear budget line. A second challenge is the uncertainty of data availability and quality; AI models thrive on large, clean datasets, yet many organisations struggle to centralise data, face regulatory restrictions or encounter hidden cleaning costs, leading to unexpected expenses when a project finally deploys. The third factor is the talent gap and hiring volatility; data scientists and ML engineers command premium salaries, and the talent market is highly competitive, so labour costs can spike and change as key hires leave or new ones are onboarded. To cope with these variables, executives can adopt a flexible strategic budgeting model that allocates a core reserve for core projects while earmarking a contingency pool for emergent technology shifts. They should also invest in robust data governance and architecture that reduces downstream cleaning costs, and create partnership agreements with talent providers or academia to secure a stable, cost‑controlled workforce. Finally, scenario‑based reviews of project milestones will surface hidden costs early, enabling timely course correction.
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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