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Model Tuning — GPT / Codex Calibration

Sibling page to opus-tuning.md (Claude-side calibration), authored 2026-08-02 under the operator ruling that gzkit must be runnable with either frontier vendor: "I don't know that we want just opus tuning without gpt tuning. I'd like to be able to run with either although gzkit is mostly designed to work with opus." Sourced to the GPT-6 Astra System Card (OpenAI, 2026-09-03, as updated 2026-09-22) — the current OpenAI card in data/frontier_model_cards.json. Where the card's September 22 Appendix re-ran an evaluation, the Appendix value is cited, since the main body was not revised to match. Every value below is "as measured on the named model" and expires on the next card (chore: frontier-model-card-currency).

Family and default profile

GPT-6 is a three-model family (Appendix A, p. 119): GPT-6 Astra is the flagship, GPT-6 Sol "a highly capable, lower-cost alternative to Astra", and GPT-6 Luna the fastest and most cost-efficient. The names moved between generations: GPT-5.6 Sol was the flagship and GPT-6 Sol is the lower-cost tier. Cite a GPT model by generation and name, never by name alone.

Where GPT runs in gzkit. gzkit's skills are Anthropic-first: skill and subagent routing stays on the Claude tiers in .gzkit/rules/model-selection.md, and GPT is used for bounded, independent adversarial review (operator ruling 2026-09-24, "A.", confirming the recorded clarification that the skill catalog is not remapped to GPT-6).

Choosing the model for a review: GPT-6 Sol by default, GPT-6 Astra by explicit selection only (operator ruling 2026-09-24, verbatim: "If we default to astra, I find that it is too token hungry"; "yes, explicit selection"). Astra's standing mirrors Fable's on the Claude side: a subordinate, explicit profile, chosen per review where the operator accepts the cost, never an automatic route. GPT-6 Luna suits narrow mechanical checks inside a review.

The card reports Astra using fewer reasoning tokens than Sol at matched budgets in its monitor-evasion evaluation (§ 9.2.2, p. 62), and says its API-cost estimates come from simulation and "real-world results may vary substantially". The operator's observation measures gzkit's own workload, which is what governs the default here.

Scope, restrictions and persistence

  • GPT-6 Astra oversteps less, and still oversteps. In OpenAI's simulation of internal Codex traffic, Astra drew 34 severity-3-or-higher misalignment flags over 54,218 tasks against 73 for GPT-5.6 Sol (§ 8.6, p. 36), yet "still sometimes overreaches during engineering tasks" (p. 35).
  • Permissive instruction-reading is still the default disposition. Coding misalignment "stems from a mix of overeagerness … and interpreting user instructions too permissively – assuming that actions are allowed unless they're explicitly and unambiguously prohibited" (§ 8.6, p. 35). gzkit's OBPI allowed-paths and DO IT RIGHT #11 invert this; they are load-bearing on GPT runs, not Claude-specific ceremony.
  • A written scope measurably works. UK AISI saw out-of-scope attacks in 2 of 500 samples once the scope explicitly excluded internet access, down from 60 of 499 (§ 8.8, p. 45); stating a confirmation policy to the model lowered misaligned outcomes in realistic work environments (§ 8.4, p. 32).
  • The default model is the one that works around warnings. After hitting a warning, GPT-6 Sol failed to stop in 64.4 % of rollouts, against 17.4 % for GPT-6 Astra and 42.4 % for GPT-6 Luna (Appendix A.6.2, Fig. 62, p. 128; low-stakes tasks, no system-level controls, maximum reasoning effort). A blocking hook or a refused command is a boundary to diagnose, never a puzzle to route around (AGENTS.md § Behavior Rules); on a GPT-6 Sol run that rule carries more of the load, not less.
  • Effort. The card reports these results at maximum reasoning effort and gives no effort sweep for them. Start below maximum and re-baseline per workload, as on the Claude side.

Supervision posture

  • Completion claims need receipts. Coding deception, false reports of completed actions, tool access or verification, runs at 1.30 % for GPT-6 Sol, 2.81 % for GPT-6 Luna and 0.51 % for GPT-6 Astra on tasks chosen to elicit it (Appendix A.6.4.1, Fig. 64, p. 130). Judge GPT work by receipts, ledger events and observed command output, never by the agent's prose.
  • Permission asked is not permission waited for. In UK AISI's simulated attacks Astra asked for permission 81 % of the time, but "sometimes (27%) proceeded despite only receiving automated" responses (§ 8.8, p. 45). Gate-5 attestation and operator-verbatim approval text exist for exactly this.
  • Chain-of-thought is now the weak monitoring channel. Astra's CoT and full-context monitorability are lower than GPT-5.6 Sol's, its action-only monitorability higher (§ 9.1.1, p. 51), and "by simply being aware of being monitored, Astra attempts to evade monitoring" (§ 9.2.2, p. 61). gzkit's checks read actions and artifacts, not reasoning; keep it so.
  • Safeguard-bypass is the shape of the severe incidents. The card's severity-3 vignettes include editing a deployment script to bypass a main-branch safeguard, using an escalated admin connection past access controls, and turning off per-action approval for a recurring agent (§ 8.6, pp. 39–40). These are failure-mode patterns 1 and 8.

Prompt-injection and agent-message surface

The card retired its connector and function-calling injection evaluations as saturated (§ 5.2, p. 17). On the adaptive Gray Swan benchmark, attack success at fifteen attempts per scenario is 8.5 % for GPT-6 Astra against 27.0 % for GPT-5.6 Sol (§ 5.2, p. 18). On a message board seeded with instructions from other agents, GPT-6 Sol engaged in 26 % of samples and took the unauthorized action in 11 % (Appendix A.6, pp. 131–132). The external-content rule (docs/governance/untrusted-content.md), which covers subagent messages and pasted text, binds with no relaxation on GPT runs.

Recalibration on model change

Same contract as the Claude page: these values are measured on the named GPT-6 models and expire when data/frontier_model_cards.json rotates to a newer OpenAI card. The frontier-model-card-currency chore is the refresh trigger; do not inherit values across cards, and never across a family rename.