# GPT-5 Mini Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/gpt-5-mini
> Markdown URL: https://aitinkerers.org/technologies/gpt-5-mini.md
> Technology record last updated: 2026-09-18T15:13:48Z
> Generated: 2026-09-20T23:34:40Z

GPT-5 Mini: The cost-optimized workhorse model, engineered for high-volume, low-latency applications that demand strong reasoning at scale.

This is the system's default workhorse: a highly efficient model balancing capability, speed, and cost. GPT-5 Mini is specifically designed for cost-optimized reasoning and high-throughput chat applications (e.g., customer service, data extraction). It maintains strong performance for well-defined tasks, offering a median throughput of approximately 170 tokens per second (TPS)—significantly faster than its predecessor, GPT-4.1. Developers benefit from a massive pricing advantage: processing four million input tokens on Mini costs the same as just one million on the flagship GPT-5. Use Mini as your primary engine for production-level tasks like code generation, structured data parsing, and conversational AI, reserving the full GPT-5 for only the most complex, multi-step agentic workflows.

- Official technology site: https://openai.com/gpt-5
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From Abacus to AI: Small Potatoes, Big Results](https://hong-kong.aitinkerers.org/talks/rsvp_3XIaboEtPiM)

I'll share my real, month-long journey as a complete non-coder who used today's AI tools (like Grok, Copilot, Claude Haiku, and especially Claude Sonnet) to build actual working things: two World of Warcraft in-game addons for my uncles to track character stats and items automatically, a "Next Bus" app for our island's private bus routes (pulling schedules from PDFs and images), and a simple dinner-bell phone app that rings my phone when someone hits bell button their phone. The talk walks through the messy reality — starting with total confusion (SQL commas feeling like ancient abacus work, AWS looking like a blurry PS2 game, pasting code line-by-line and debugging parentheses I didn't understand), failing a lot, switching models when one got stuck, and eventually getting functional apps and addons. I'll show how I "failed faster" by iterating quickly, using my own low-tech version control (100+ numbered folders), and leaning on different AIs for different strengths. It's not about becoming a pro developer overnight — it's about an ordinary person getting useful results with zero prior experience.

- Event context: AI Tinkerers Hong Kong GBA at the Hive: Creative AI Demos & Technical Show-and-Tell — 2026-03-26 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_3XIaboEtPiM

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