GPT-5.6 Is Live: Sol vs Terra vs Luna — July 2026 Comparison

• Routing everything to Luna because it is the biggest. Luna bills at the highest input tier and runs slower. Most support tickets and codegen loops do not need 1.5M tokens—Sol or Terra will answer faster and cheaper.
• Assuming Sol is just a renamed GPT-5.5. Sol shares the alignment fix but trims reasoning depth for speed. Agent tool graphs that worked on Terra may fail on Sol when parallel MCP calls exceed eight steps.
• Testing all three variants on your daily Mac with one production key. July GA week invites parallel evals. Mixing Sol latency tests, Luna full-repo dumps, and production vault access on a laptop is how security reviews replace launches.

For GA timeline context, see our OpenAI July 2026 roundup and GPT-5.6 launch window guide.

OpenAI did not ship three random SKUs. Each name maps to a compute profile inside the GPT-5.6 family—all sharing the July alignment fix, structured output GA, and parallel tool graph v2.

• Sol (gpt-5.6-sol) — Optimized for time-to-first-token. Targets sub-200 ms TTFT on 4K prompts. Context capped at 128K. Best for ChatGPT Advanced Voice, Realtime API v2 streams, and high-QPS chat backends.
• Terra (gpt-5.6 / gpt-5.6-terra) — The GA default most teams should start with. 512K context, balanced reasoning, standard agent mode. ChatGPT Plus and Team tiers route general queries here unless you pick Luna manually.
• Luna (gpt-5.6-luna) — The 1.5M-context lane. Slower TTFT (~420 ms) but highest instruction-following scores on full-repo and compliance workloads. Enterprise tier unlocks first; API wide by mid-July.

Rule of thumb: Sol for speed, Terra for daily work, Luna only when the prompt truly needs more than 512K tokens or maximum alignment sensitivity.

Quick verdict: Start July on Terra. Add Sol for latency-sensitive surfaces. Promote Luna only after cost models prove 512K is insufficient. Validate all three on a rented M4 node before touching production routing rules.

1. Inventory workloads by context and latency. Tag each pipeline: under 32K + low latency → Sol; daily agents → Terra; full-repo or compliance → Luna candidate.
2. Freeze GPT-5.5 baseline scores. Record tool success rate, refusal accuracy, p95 latency, and cost per task. You need before/after numbers for each variant.
3. Build a 50-case eval pack. Cover instruction following, eight- versus sixteen-step tool graphs, 128K/512K/1.5M context boundaries, and alignment edge cases.
4. Provision an isolated MacPull lab. Rent a Mac mini M4 (24 GB recommended) with separate API keys, OpenAI SDK 2.x, and routing scripts for all three endpoints—no production vault.
5. Canary 5% traffic per variant. After July 1 GA, replay identical agent traces on Sol, Terra, and Luna. Compare scores, bills, and p95 latency side by side.
6. Lock routing rules and spend caps. Define automatic fallback Luna → Terra → Sol, daily token ceilings per SKU, and rollback to GPT-5.5 before GA week ends.

• July 1, 2026 — GPT-5.6 GA with three public variants: Sol, Terra (default), and Luna. Preview endpoints deprecated for Tier-5 accounts.
• 128K / 512K / 1.5M — Context ceilings for Sol, Terra, and Luna respectively. Long-context surcharges apply above 128K on Terra and Luna only.
• 18% — Reported instruction-following gain vs GPT-5.5 on Terra at GA, with Luna adding ~4 pp on alignment-sensitive evals.
• $1.80–$4.00 — July input price range per 1M tokens across Sol → Luna. Output scales proportionally; budget Luna jobs separately.
• 24 GB unified memory — MacPull recommended floor for running SDK eval scripts, Docker MCP sandboxes, and local MLX fallbacks on one M4 node while testing all three API variants.

GPT-5.6 GA is not one model—it is three lanes. Sol wins on speed, Terra is your July default, and Luna owns the 1.5M-context jobs that actually need it. Teams that route by workload adopt cleanly; teams that pick the biggest SKU everywhere discover July invoices before they discover better answers.

The practical path: benchmark all three on isolated hardware, promote Terra to production, add Sol where latency matters, and gate Luna behind context allowlists and spend caps.

Ready to compare Sol, Terra, and Luna before July routing goes live? Rent MacPull Mac mini M4 (24 GB + 512 GB recommended for multi-variant agent sandboxes), compare plans on pricing, and follow SSH setup in help. Run side-by-side GPT-5.6 evals on dedicated Apple Silicon—then pick Sol, Terra, or Luna with data, not guesswork.