The Daily AI Show When to use OpenAI's latest models: 4.1, o3, and o4-mini (Ep. 444)
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Intro
With OpenAI dropping 4.1, 4.1 Mini, 4.1 Nano, O3, and O4-Mini, itโs been a week of nonstop releases. The Daily AI Show team unpacks what each of these new models can do, how they compare, where they fit into your workflow, and why pricing, context windows, and access methods matter. This episode offers a full breakdown to help you test the right model for the right job.
Key Points Discussed
The new OpenAI models include 4.1, 4.1 Mini, 4.1 Nano, O3, and O4-Mini. All have different capabilities, pricing, and access methods.
4.1 is currently only available via API, not inside ChatGPT. It offers the highest context window (1 million tokens) and better instruction following.
O3 is OpenAIโs new flagship reasoning model, priced higher than 4.1 but offers deep, agentic planning and sophisticated outputs.
The model naming remains confusing. OpenAI admits their naming system is messy, especially with overlapping versions like 4.0, 4.1, and 4.5.
4.1 models are broken into tiers: 4.1 (flagship), Mini (mid-tier), and Nano (lightweight and cheapest).
Mini and Nano are optimized for specific cost-performance tradeoffs and are ideal for automation or retrieval tasks where speed matters.
Claude 3.7 Sonnet and Gemini 2.5 Pro were referenced as benchmarks for comparison, especially for long-context tasks and coding accuracy.
Beth emphasized prompt hygiene and using the model-specific guides that OpenAI publishes to get better results.
Jyunmi walked through how each model is designed to replace or improve upon prior versions like 3.5, 4.0, and 4.5.
Karl highlighted client projects using O3 and 4.1 via API for proposal generation, data extraction, and advanced analysis.
The team debated whether Pro access at $200 per month is necessary now that O3 is available in the $20 plan. Many prefer API pay-as-you-go access for cost control.
Brian showcased a personal agent built with O3 that created a complete go-to-market course, complete with a dynamic dashboard and interactive progress tracking.
The group agreed that in the future, personal agents built on reasoning models like O3 will dynamically generate learning experiences tailored to individual needs.
Timestamps & Topics
00:01:00 ๐ง Intro to the wave of OpenAI model releases
00:02:16 ๐ OpenAIโs model comparison page and context windows
00:04:07 ๐ฐ Price comparison between 4.1, O3, and O4-Mini
00:05:32 ๐ค Testing models through Playground and API
00:07:24 ๐งฉ Jyunmi breaks down model replacements and tiers
00:11:15 ๐ธ O3 costs 5x more than 4.1, but delivers deeper planning
00:12:41 ๐ง 4.1 Mini and Nano as cost-efficient workflow tools
00:16:56 ๐ง Testing strategies for model evaluation
00:19:50 ๐งช TypingMind and other tools for testing models side-by-side
00:22:14 ๐งพ OpenAI prompt guide makes big difference in results
00:26:03 ๐ง Carl applies O3 and 4.1 in live client projects
00:29:13 ๐ ๏ธ API use often more efficient than Pro plan
00:33:17 ๐งโ๐ซ Brian demos custom go-to-market course built with O3
00:39:48 ๐ Progress dashboard and course personalization
00:42:08 ๐ Persistent memory, JSON state tracking, and session testing
00:46:12 ๐ก Using GPTs for dashboards, code, and workflow planning
00:50:13 ๐ Custom GPT idea: using LinkedIn posts to reverse-engineer insights
00:52:38 ๐๏ธ Real-world use cases: construction site inspections via multimodal models
00:56:03 ๐ง Tip: use models to first learn about other models before choosing
00:57:59 ๐ฏ Final thoughts: ask harder questions, break your own habits
01:00:04 ๐ง Call for more demo-focused โBe About Itโ shows coming soon
01:01:29 ๐ Wrap-up: Biweekly recap tomorrow, conundrum on Saturday, newsletter Sunday
The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh