Efficiency is the next frontier in LLMs, and I'm not confident the American companies are taking it seriously enough. DeepSeek, even in a naive API-calling loop, serves something like 80-90% cached tokens at an absurdly low price per token. Using an agent harness tuned specifically for their caching (Reasonix) pushes the cached tokens to 97-99%. DeepSeek is consistently among the cheapest models per task in my benchmarks, while also performing quite well. I'm still almost always using Claude for work, but for side projects, small stuff, etc. and anything better served by an API rather than starting up Claude Code (or `claude -p`) I'm using DeepSeek pretty often.

Anthropic models also shut down on a lot of security-related work, which is what I've been spending a lot of time on lately. I expected Fable to refuse this kind of task, but even Opus 4.8 refuses to build a verification harness for security bugs, as that involves exercising a discovered bug to prove it's been fixed in an automated red/green way, which looks like exploit creation to Opus' guardrails. So, I have to use other models for that work, now, though most of the original benchmarks I built were built with Claude.