5 entries
A local guardrail can approve a task that the server will cut off before its warning threshold. Reconcile server windows, per-model limits and locally valued usage before trusting it. The September observations show how a stale catalog can break that protection.
By mid-2026, the reference LLM leaderboards have frozen or pivoted, and what stays current is either self-reported by vendors or recopied by unverified aggregators. One independent harness still holds — and it saturates at the top. Result: the source of truth left is your own codebase. Here's how I decide without leaning on numbers nobody can verify anymore.
Under a budget-capped subscription like opencode Go, picking a model isn't about token price but requests per 5 hours, the one quality source still alive (vals.ai), and tool-calling reliability — the decisive criterion no benchmark measures. The result: one model per preset, a frontier delegated outside the agent, and tools constrained rather than instructed.