Grok 4.5: Finding the production fit

Point your harness at grok-4.5: xAI's docs place it as the current Grok generation across chat, files, connected tools, and multimodal work. Pricing and aliases can move by the time you read this, so check again before you wire anything into production.

The mistake is starting from a leaderboard screenshot instead of your own traffic. A leaderboard doesn't know your latency budget, your context sizes, or what a wrong answer costs. Your queue does.

grade the patch, not the prose

Decide what done means before the first prompt goes out. A frontier model sounds confident whether or not it's right, so the bar can't be about tone:

• extraction passes when the record is schema-valid and traces to source text
• coding passes when the patch clears tests and review
• research passes when the claims carry citations that check out

Bucket real requests by difficulty, latency, context size, and cost of a mistake, then compare pass rates, not impressions, with the request and the harness held constant: same tool schemas, sampling settings, output caps.

model ID and dated API version
prompt and tool-schema revision
input, cached input, reasoning, output tokens
queue time, first token, total completion
pass, retry, abstain, human repair
provider error and fallback route

context windows don't waive the rules

A bigger window doesn't buy you out of prefill time, or stop retrieval noise from drowning your instructions under documents you didn't need to send. Put stable content first for caching, keep volatile metadata off the cache boundary, and send a checkpoint instead of full history when you can. Ask for the decision and the evidence, not a tutorial nobody asked for.

Tool calling moves the risk, it doesn't remove it. The application still checks arguments, confirms the caller is allowed to do this, caps the blast radius, and logs what happened. A page it fetched, an email in its context: that's data, not an instruction it gets to obey.

give it a lane, pin the version

Write the routing rule into config, not into whichever model is fashionable this month, the same way you'd turn down a model dial for routine calls. Cheap-and-fast handles routine volume; escalation fires on a named condition, not a hunch. A cheap tier can cost more in retries and review than it saves.

Pin the dated version where it matters, and treat migration as ops the way model migration reality tends to insist on sooner or later: canary new aliases before full traffic, keep a known-good fallback wired in, re-run the eval set the moment the prompt or tool contract shifts under you.

What I still don't have a clean answer for: how often that re-run should trigger on its own versus wait for someone to notice the numbers drifted. I've watched both defaults burn people.