#modelselection

Benchmarks, price, latency, context: a working decision framework instead of hype.

DeepSeek's official V4 Flash 0731 beta and OpenAI's 80% Luna price cut redraw the practical price-performance frontier for production agent fleets.

Discover the benefits of using Switchyard for large language model applications, boosting model performance by up to 30% and improving outcomes in various tasks

On one RTX 5090 workshop, a 4B model beat a 26B model on speed while both passed four code checks. Here is the model-selection rule I kept.

The AI Model Selection Decision Tree: How to Choose the Right Model from $0.05/M to...

Three different families of vision model solve overlapping but distinct problems. A decision guide based on what you're actually trying to build.

License, size, context length, existing instruction-tuning and community tooling — the factors that matter more than a leaderboard rank when choosing

A local model can fit in VRAM, download cleanly, and still fail before the first token. My 5090 test adds runtime support as a separate gate.

A practical guide to choosing between OpenAI and Claude for your business AI agent, with verified August 2026 pricing, a modelled monthly cost table, and the decision framework I use across 126 production builds.

A practical decision tree for agentic coding: pick local models by repo size and VRAM tier (16GB/24GB/48GB+/CPU), with context, tool-calling reliability, and quantization rules that actually hold up.

Three Llama 3.1 8B runs missed a 400-word floor. Here is the verifier-driven route that moved long-form synthesis to Gemma 4 26B.

A 6.66 GiB ternary 27B model fit my RTX 5090, but it lost the default slot. File size is only the first local model-selection gate.