Today the radar is full of model launches, but what caught my attention was not raw capability. It was the strategy behind them. I cover the main story first, then everything else that passed the filter.

In brief

The new GPT-5.6 family reinforces a practical product decision: combine models by workflow stage. The more expensive model belongs where reasoning and precision matter; faster, cheaper options take on repetitive, high-volume work.

Anyone who works on credit products learns one thing early: there is almost never a single “best” solution. There is the best solution for each use case.

I was reminded of that when I read about OpenAI’s new GPT-5.6 family. Instead of one model, it comes in three: a more expensive, sharper option for reasoning and difficult problems; a middle option with good quality at half the cost; and a fast, inexpensive one designed for high volume.

It may sound like a technical detail, but the message is commercial. The conversation has moved from “which AI do I use?” to “which AI do I use for each part of the workflow?”

In my world, that makes complete sense. One stage requires precision and cannot afford mistakes. Another is a repetitive, high-volume task where speed and cost matter much more. Treating everything with the same tool is expensive and, ultimately, architectural laziness.

For people leading product, technology, or operations, this changes how they think about cost. You can design AI product features by combining models in layers: put the expensive one only where it delivers real value and let the cheaper one handle the rest. It is the same efficiency logic we already pursue in any financial process.

This choice also belongs in the design of AI agents: each stage needs a model that fits its risk, volume, and type of decision. When an agent takes consequential actions, such as in financial operations, cost is not the only criterion; permissions and AI governance are part of the architecture too.

I like this shift because it takes AI out of the realm of hype and puts it in the realm of practical decisions: where to invest, where to save, and what to test first. That is where professionals who understand the business make a real difference, choosing well rather than buying the most expensive option for comfort.

For anyone curious about the details of each version and the pricing, the full release is here.

The rest of the radar

Step 3.7 Flash — another low-latency, low-cost provider for AI features that are sensitive to response time. Read more

Dynamic Workflows in Claude Code — dynamic workflows bring coding agents closer to real workflows and speed up prototyping and automation. Read more

Cactus Hybrid (Gemma 4) — confidence-based routing between on-device and cloud cuts cost and improves the perceived reliability of AI. Read more

Noisy LLM evaluators can still help — agents can improve with imperfect automated evaluation instead of waiting for the perfect benchmark. Read more

GigaToken — tokenization roughly 1,000x faster addresses a real cost and throughput bottleneck in AI pipelines. Read more

Robinhood enables trading through AI agents — a real case of an agent taking consequential action with money, and a strong study in guardrails, permissions, and accountability. Read more

Frigade “Skills” — an assistant that performs actions inside the product without code, lowering the cost of embedding actionable AI in the experience. Read more

Bento (Show HN) — an entire presentation in a single HTML file, a lesson in product simplicity and frictionless distribution. Read more

AI for PMs by workflow stage — the 2026 recommendation is to build the stack by stage, starting with discovery, and avoid all-in-one suites. Read more

That is all for today. Choosing the right tool for each stage continues to matter more than having the most expensive one.