Is model choice the only free lunch in AI? (www.educative.io)

AI model choice is exploding — Hugging Face now hosts 1.7M models and major vendors (OpenAI, Google, xAI, Amazon) are releasing multiple families and versions rapidly. Salman Paracha argues this abundance is a near “free lunch”: instead of one monolithic winner, builders can pick fit-for-purpose models (reasoning, summarization, coding) to get better accuracy, throughput, or cost for each task. That trend is enabling faster experimentation, less vendor lock-in, and more rapid adoption of improvements — provided teams have a simple adoption layer to avoid drowning in options. Technically, Paracha prescribes a two-part adoption infrastructure: (1) lightweight testing — templatized fixtures (10–20 examples per task), deterministic checks (JSON/schema validation, anchor tokens), plus optional LLM-as-judge or human review; and (2) a high-performance proxy (archgw) that exposes intent-based aliases (e.g., arch.summarize.v1) and routes them to underlying models with traffic-splitting, fallbacks, and centralized guardrails. He provides a Python bench harness (bench.py) that hits archgw via an OpenAI-compatible client, validates schema and anchors, measures latency, and defines success thresholds (≥90% schema-valid, ≥80% anchors). An example archgw config maps aliases to gpt-4o-mini and o3; sample results show summarize is faster but less reliable, while reason is slower but more accurate. The approach yields decoupled code, safe canaries, unified observability, and quota/policy enforcement by intent rather than vendor.