GPT-5.6 Luna Low-Risk First-Pass and Escalation Routing Workflow

One-sentence takeaway

Put Luna on low-risk first-pass work such as summaries, labels, lightweight review prechecks, and scaffolding, then escalate failed or complex tasks to Terra/Sol. This is CodeRabbit's reusable routing recommendation for three tiers of coding agents.

Use cases

• Suitable tasks: PR summaries, simple code explanations, test-name generation, changelog drafts, low-risk review prechecks, and repetitive transformations that can be verified with unit tests.

• Unsuitable tasks: cross-file architecture, long-running autonomous implementation, final high-risk security reviews, and open-ended tasks without definable pass conditions.

• Applicable model version: GPT-5.6 Luna. The article also discusses Terra and Sol, but does not run a quantitative coding task independently on Luna.

• Applicable client, agent, or API: CodeRabbit-style PR review/code agents; the approach can also be migrated to a self-built gateway.

• Recommended reasoning tier and parameters: use lower reasoning for the first pass; have Terra/Sol rerun the task at a quality gate or during escalation, with the specific tier calibrated against the project's acceptance suite.

Ready-to-use content

Organized into routing rules based on CodeRabbit's workflow map:

if task in {summary, simple_explanation, pr_summary,
            lightweight_review_precheck, test_name, changelog_scaffold}:
    run Luna first
    require a bounded output and a deterministic check
    if check fails, fields are missing, or scope expands:
        escalate to Terra

if task is scoped implementation or review triage:
    try Terra
    keep escalation to Sol available

if task requires multi-file persistence, long task lists,
architecture judgment, or final high-risk review:
    route Sol or a separately validated frontier model

Test/workflow steps

1. Define the output schema, maximum tokens, permitted modification scope, and pass/escalation conditions for the first-pass task.

2. Have Luna only read, summarize, classify, or make small reversible changes; record the first-pass success rate, number of reworks, and total cost of each result.

3. Escalate immediately when fields are missing, tests fail, cross-file dependencies appear, repeated loops occur, or the scope expands; do not let Luna retry indefinitely.

4. Track the results from Terra/Sol after escalation separately; do not combine first-pass and escalation tokens into Luna's single-run cost.

5. In PR review scenarios, retain the model's original comments, filter out low-confidence findings and nitpicks first, and then send actionable issues to a human.

6. Sample-review Luna's missed findings and false positives each week; withdraw this routing if the cost of human corrections for low-risk tasks exceeds the token savings.

Original evidence and data

• CodeRabbit positions Luna as a low-reasoning, high-volume lane, with examples including quick summaries, simple code explanations, PR summaries, lightweight review prechecks, test-name generation, and changelog scaffolds.

• For Sol/Terra's long-running coding run (100+ tasks), the same article reports a 63.7% pass rate and average output of 20,968 tokens for Sol, versus 40.7% and 55,594 tokens for Terra. Luna did not participate in this quantitative long-running run.

• The same article's CodeRabbit review benchmark also leaves Luna out of the final table: Sol records 69/99 actionable passes, or 69.7%; Terra records 53/101, or 52.5%.

• Therefore, the Luna conclusion in this workflow comes from the authors' model map and routing recommendation, not from independent evidence of Luna's pass rate.

Scope boundaries

• The article's quantitative results mainly concern Sol/Terra and cannot be used to claim a coding pass rate for Luna.

• CodeRabbit's evaluation is conducted by the product vendor; it controls the task set, filters, baseline ensemble, and comment definitions. Self-test before migrating the workflow across projects.

• “Low-risk first pass” must be defined by the business. Once summaries or classifications drive payment, compliance, or security decisions, they are no longer low-risk.

• Routing escalation should have a budget, stop conditions, and a human rollback path, preventing a cheap first pass from becoming a more expensive total workflow through repeated failures.

Source excerpt or observation (compliance short quote only)

The authors recommend “Use Luna as a first-pass lane,” but the same article's quantitative coding data did not measure Luna; the two should not be conflated into a single benchmark conclusion.