ThumbGate Verification evidence
guide | gpt-5.5 model evaluation

# Evaluate GPT-5.5 Before You Route Production Agent Work

Frontier-model upgrades can improve coding, dataset analysis, and dashboards, but the ROI comes from measured routing. ThumbGate adds a model-candidate workload so teams can benchmark GPT-5.5 against real feedback, gate evals, and dashboard-analysis criteria before changing defaults.

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## Why this page exists

- GPT-5.5 should be treated as a frontier candidate for complex work, not a blanket replacement for every cheap gate.
- The highest ROI path is benchmark-first routing: keep cheap tiers for simple checks and escalate dataset, dashboard, and long-context work when evidence supports it.
- ThumbGate now exposes a dashboard-analysis workload through npx thumbgate model-candidates --workload=dashboard-analysis --provider=openai --json.

## What changed

OpenAI positions GPT-5.5 for complex reasoning, coding, data analysis, and tool-using work. Julius framed the same model around dataset analysis, charts, dashboards, insight quality, and code generation.

For ThumbGate, the useful product move is not to rewrite every default. It is to make model adoption measurable: define the workload, pick candidate models, run existing evals, and route only the work that earns the frontier spend.

## What ThumbGate adds

- A GPT-5.5 model candidate in config/model-candidates.json with long-context, data-analysis, dashboard-creation, charting, tool-use, and reliability strengths.
- A dashboard-analysis workload with metrics for insight accuracy, chart-spec validity, dashboard completeness, long-context reliability, latency, and cost per analysis.
- A CLI path: npx thumbgate model-candidates --workload=dashboard-analysis --provider=openai --json.
- A tier-router config that pins the frontier tier to gpt-5.5 while keeping explicit cheaper tiers for fast, low-cost work.

## Where this creates ROI

This gives platform teams a defensible answer to "should we move to GPT-5.5?" Run the candidate report, attach gate and benchmark evidence, and only then route high-value analytical or long-context agent tasks to the frontier tier.

The commercial wedge is a Workflow Hardening Sprint focused on model routing: define which workflows deserve frontier spend, which stay on cheap tiers, and which require pre-action checks before a model can touch live systems.

## FAQ

Should every ThumbGate task use GPT-5.5?

No. Cheap gates, classification, extraction, and simple triage should stay on lower-cost tiers when they pass evals. GPT-5.5 is best evaluated for complex reasoning, long-context, coding, dataset, and dashboard work.

How do I benchmark GPT-5.5 in ThumbGate?

Run npx thumbgate model-candidates --workload=dashboard-analysis --provider=openai --json, then use the emitted benchmark commands and metrics to compare insight quality, chart validity, latency, and cost before changing routing defaults.

Does this automatically call the OpenAI API?

No. The catalog is an evaluation and routing surface. It records candidate models and benchmark plans without assuming provider credentials or silently changing runtime behavior.