2026 Frontier Model Developer Playbook (ICMD)

Use this checklist to choose between OpenAI, Anthropic, and Google DeepMind (and to avoid betting your product on any single provider).

1) Define the job to be done (before model shopping)
- Write the top 10 user workflows as “inputs → expected outputs → allowed actions.”
- Label each workflow by risk tier: low (internal), medium (customer-facing), high (regulated/financial/medical).
- Set a hard latency budget (p95) and a hard cost budget (cost per successful task).

2) Create a routing matrix (multi-model by default)
- Pick a “fast/cheap” model class for routing, classification, and extraction.
- Pick a “frontier” model class for complex reasoning and agent planning.
- Decide which workflows require multimodal (images, audio, files) and isolate them.
- Add fallback rules: if provider A errors or exceeds latency, retry once on provider B.

3) Establish your internal model contract
- Normalize: text output, optional JSON output, tool calls, and usage accounting.
- Enforce structured outputs with JSON schema validation.
- Log: prompt version, model name, tool calls, latency, token usage, and final outcome.

4) Build evals that match production
- Golden set: at least 200 real examples per major workflow.
- Metrics: task success rate, schema validity, tool-call correctness, p95 latency, cost per success.
- Red-team set: prompt injection attempts, PII leakage probes, policy edge cases.
- Run nightly regression tests across at least two providers.

5) Control tool permissions like an enterprise system
- Default to read-only tools; require explicit escalation for write actions.
- Use least privilege per workflow (allowlist tools + fields).
- Add “human-in-the-loop” gates for high-risk actions (payments, account changes, legal output).
- Record audit logs that map: user → prompt → model → tool action → result.

6) Make unit economics visible
- Track cost per successful task (not just cost per token).
- Cap retries and add deterministic validation/repair steps.
- Cache repeated prompt prefixes and retrieval results; measure cache hit rate weekly.
- Maintain a monthly cost forecast: requests × avg tokens × avg tool calls × retry rate.

7) Vendor due diligence (minimum bar)
- Data retention and “training on your data” policy in writing.
- Region availability and processing location for regulated customers.
- Incident response and uptime expectations; plan for partial outages.
- Procurement fit: can your customers buy this vendor quickly (existing cloud agreement, DPA)?

If you do nothing else: implement a router + internal contract + eval suite. That trio is what keeps teams shipping when models, pricing, and policies shift in 2026.