Quick answer
- GPT-5.6 Sol price
- $5 in / $30 out per MTok
- GPT-5.6 Terra price
- $2.5 in / $15 out per MTok
- GPT-5.6 Luna price
- $1 in / $6 out per MTok
- Terminal-Bench 2.1
- Sol 88.8% › Terra 87.4% › Luna 84.7% (OpenAI launch materials)
First, the Naming Scheme
OpenAI's GPT-5.6 launch on July 9, 2026 came with a naming scheme that finally makes the lineup legible: the number is the generation, the name is the capability tier. GPT-5.6 is the generation; Sol, Terra, and Luna are the tiers within it, flagship, workhorse, and budget/fast respectively. The API identifiers follow the same pattern: gpt-5.6-sol, gpt-5.6-terra, gpt-5.6-luna.
That means this is not a comparison of three different models so much as three price points on one generation. All three went generally available the same day and all three are selectable in Codex. The question is not “which is best”, Sol is, by construction, but which one each unit of work deserves. Single-model deep dives: Sol, Terra, and Luna.
Head-to-Head: The Verified Data
| GPT-5.6 Sol | GPT-5.6 Terra | GPT-5.6 Luna | |
|---|---|---|---|
| Role | Flagship | Workhorse | Budget / fast |
| Price (per MTok) | $5 in / $30 out per MTok | $2.5 in / $15 out per MTok | $1 in / $6 out per MTok |
| Terminal-Bench 2.1 | 88.8% (91.9% in ultra mode) | 87.4% | 84.7% |
| Max reasoning effort | Yes | No | No |
| Ultra mode (parallel subagents) | Yes | No | No |
| Released | July 9, 2026 | July 9, 2026 | July 9, 2026 |
Data verified July 18, 2026 against OpenAI's published pricing and launch materials. Terminal-Bench 2.1 is the one benchmark OpenAI published across all three tiers, which makes it the family's only like-for-like yardstick, and worth reading carefully, next.
Price vs Benchmark: Where the Gaps Actually Are
Put the two ladders side by side and the family's economics jump out. From Terra to Sol, the benchmark gap is 1.4 points (87.4% to 88.8%) and the price gap is 2x ($2.5 to $5 per MTok in, $15 to $30 out). From Luna to Terra, the benchmark gap is 2.7 points (84.7% to 87.4%) for a 2.5x price gap ($1 to $2.5 in, $6 to $15 out).
Read that way, the Luna-to-Terra step buys nearly twice the benchmark improvement of the Terra-to-Sol step. That is why Terra is the default: it sits at the top of the steep part of the curve, where each dollar still buys measurable capability, while Sol's premium pays for the last 1.4 points plus its exclusive features. How this slots into the wider Codex lineup, including GPT-5.5, is covered in the best Codex model guide.
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What Only Sol Can Do
The benchmark gap understates the tier gap in one respect: two capabilities exist only on Sol, at any price. Max reasoning effort is the family's highest thinking budget, for problems where you want the model to grind. Ultra mode runs native parallel subagents, Sol fans a task out to multiple copies of itself working concurrently, and per OpenAI's launch materials it lifts Sol's Terminal-Bench 2.1 score from 88.8% to 91.9% in ultra mode.
If your workload needs either, the tier question answers itself, Terra and Luna are not discounted versions of these features, they simply do not have them. If your workload does not need them, the 1.4-point benchmark gap is the honest measure of what Sol's 2x premium buys.
The Decision Rule, Step by Step
- 1
Start every task on Terra
The workhorse default: roughly GPT-5.5 performance at about half the price ($2.5 in / $15 out per MTok), and within 1.4 points of the flagship on the family's only shared benchmark. Most Codex sessions should begin and end here. - 2
Drop to Luna when the task is mechanical
Lint fixes, renames, formulaic edits, high-volume batch work: if the spec fully determines the diff, Luna's $1 in / $6 out per MTok does the job at 2.5x below Terra's price. Never send it work hard enough to fail, a failed cheap run plus a re-run costs more than Terra succeeding once. - 3
Escalate to Sol when failure is expensive
Hard cross-cutting problems, long agentic runs, terminal-heavy chains, and anything that wants max reasoning effort or ultra mode. At $5 in / $30 out per MTok the premium is real, and worth it exactly when a Terra failure would cost more than the price difference. - 4
Let outcomes tune the routing
Track which tier each kind of task actually succeeds on. If Terra keeps failing a category, that category is Sol work; if Luna never fails one, Terra was overspend. The rule improves with every merged diff.
Routing Per Story, Not Per Project
The tier decision is cheapest when it is made per unit of work rather than once per project, and that is an orchestration problem more than a model problem. AIDEN's approach: your Codex CLI runs on a kanban board where every story is a card with its own model, its own branch, and its own diff, so the mechanical cards run Luna, the defaults run Terra, the hard ones run Sol, and a wrong routing costs you one card, not a sprint. The same board also runs Claude Code side by side, if the answer to “which tier” turns out to be “a different vendor for this story”, the full field is at the models hub.