When Ploy swapped its production marketing‑site AI agent from Claude Opus 4.8 to OpenAI’s GPT‑5.6 Sol, the raw numbers were striking: average build time fell from 8 minutes to 3 minutes 42 seconds (2.2× faster) and token‑based cost dropped from $3.06 to $2.22 per page (27 % cheaper) [1]. Those headline figures, however, only materialize after fixing three hidden friction points that most enterprises overlook when moving between frontier models.

1. Re‑calibrate your evaluation harness

Ploy’s test suite runs the agent against real workspaces, scoring visual fidelity, content accuracy, and tool‑trajectory compliance. The first cross‑model run flagged a third of failures as harness mismatches rather than model regressions. Claude’s sequential tool calls fit the suite’s budget, while GPT‑5.6 issued parallel calls and batched file reads that the executor could not handle [1]. The lesson for IT leaders: audit the trace data before trusting pass rates; a model may appear unstable simply because the test harness assumes the incumbent’s behavior.

2. Align tool‑call schemas with OpenAI’s function‑calling semantics

GPT‑5.6 automatically populated all 25 optional parameters in Ploy’s code tool, inserting placeholder values like offset: 0. Those invented values masqueraded as legitimate arguments, causing 52 % of file‑read calls to return empty results [1]. OpenAI’s strict mode does not suppress this behaviour. The solution was a provider‑specific schema transform: make optional fields required‑but‑nullable (anyOf: [T, null]) at the API boundary, then strip nulls before the tool executes. This eliminated spurious calls and reduced overall tool‑call volume by roughly 30 %.

3. Redesign prompt‑caching for OpenAI’s new model

Claude allowed organization‑wide cache hits on a 29 K‑token static prefix, yielding 92‑96 % hit rates. GPT‑5.6 dropped partial‑prefix matching; only entire prompts keyed by prompt_cache_key are cached, and each key is limited to ~15 req/min [2]. A naïve per‑conversation key resulted in 0 % first‑call hits, inflating costs. Ploy settled on a workspace‑scoped key, sharing the static prefix across all sessions in a customer’s workspace while keeping per‑key traffic under the node limit. After this change, first‑call cache hits rose to 83.7 %, uncached input tokens fell 28 %, and the total cost undercut Claude’s baseline [1].

4. Make reasoning replay self‑contained

OpenAI’s Responses API defaults to server‑side item references for prior‑turn reasoning. Ploy experienced intermittent Item 'rs_…' not found errors when the server state expired. Setting store: false forces the SDK to return encrypted reasoning blobs, eliminating the dependency on mutable server state and stabilising long conversations [1].

Business impact

For a SaaS that builds and edits live marketing websites, the combined gains translate to tangible ROI:

  • Reduced compute spend: $0.84 saved per page, scaling to $84 K annually on 100 K builds.
  • Faster time‑to‑publish: sub‑four‑minute builds improve user satisfaction and enable higher concurrency without additional hardware.
  • Lower operational risk: fewer tool‑call failures and deterministic caching reduce support tickets and on‑call load.
  • Strategic flexibility: the migration framework (harness audit, schema transform, cache key design) is reusable for future model upgrades, protecting the organization from vendor‑specific surprises.

Enterprises considering a similar shift should allocate time for evaluation hygiene, schema adaptation, and cache architecture before benchmarking model pricing. Those hidden costs can eclipse the advertised per‑token rates and erode the promised savings.