Great analysis — this mirrors almost exactly what we've been studying in production at a regulated financial institution in Southeast Asia (banking sector).

The "token price is a poor indicator of actual costs" point is the core insight most teams miss. What you're observing with GLM-5.2 is what happens when a model's reasoning architecture aligns well with the task structure: it hits a sweet spot where fewer tokens are needed precisely because it reasons more directly. The cost-efficiency relationship is nonlinear — a model that is 3x cheaper per token but 2x more verbose per task saves you nothing; GLM-5.2 wins on both dimensions simultaneously, which is rare.

A few angles worth adding to your framework:

1. Self-hosting changes the break-even math at scale

At 10K+ tasks/day, the API pricing story shifts materially. A GLM-5.2 deployment on 8×H100s runs roughly $55–70K/month depending on cloud and reservation type. At your $1,280/day API rate scaled to 10K tasks, that is ~$38,400/month — so self-hosting breaks even around month 6, after which marginal inference cost drops 70–80%. For regulated industries with data residency requirements, the compliance value is additive on top of that.

The practical catch: cold-start latency for a 753B MoE model is non-trivial. Plan for warm routing — keep a persistent serving instance for daytime load, use spot capacity for batch overnight jobs.

2. Model routing on Databricks — practical implementation

The tiered routing you describe (Flash → GLM-5.2 → Claude) maps cleanly to Databricks AI Gateway with custom routing logic:

• Host GLM-5.2 via Mosaic AI Model Serving (or proxy to external GLM API endpoint)
• Add a lightweight classifier (a 1B BERT-class model is sufficient) as the routing layer — predicts task complexity from prompt features like length, entity density, and instruction type
• Route to Claude only when classifier confidence + task-type signal crosses a threshold

We are seeing 60–70% of enterprise AI tasks land in the "standard" tier in practice. The 15–20% hardest tasks — multi-repository dependency resolution, architecture decisions, ambiguous multi-step reasoning — are where frontier models still earn their premium.

3. The regulated-industry sovereignty argument is underweighted

Your sovereignty point deserves more emphasis. For banking and financial services it is not just about data residency — it is about auditability. MIT-licensed open weights let you reproduce any inference exactly, version model checkpoints alongside your model risk frameworks, and demonstrate to regulators precisely what the model saw and how it responded. That is structurally impossible with black-box API calls where model versions drift silently.

One open question for anyone running this in production: how are you handling session-level cost attribution across business units? When multiple teams share a single serving endpoint, allocating inference costs back to cost centers is where we see the most operational friction. Would be interested to hear how others are solving this.