Model guide

GPT-5.6 Terra: The Codex Workhorse, Explained

Sol got the launch keynote, but Terra is the tier most Codex sessions should actually run on: roughly GPT-5.5 performance at about half the price. Here is the verified data, what Terra lacks, and the escalation rule.

By Kylian Migot · Updated July 2026 · 6 min read

Quick answer

GPT-5.6 Terra is the workhorse tier of OpenAI's GPT-5.6 family and the rational default for most Codex sessions: roughly GPT-5.5 performance at about half the price ($2.5 in / $15 out per MTok). It scores 87.4% on Terminal-Bench 2.1, just 1.4 points behind Sol, which costs 2x more. What it lacks: Sol's max reasoning effort and ultra mode.
What it is
Workhorse tier of the GPT-5.6 family (Sol › Terra › Luna)
Price
$2.5 in / $15 out per MTok
Headline score
87.4% on Terminal-Bench 2.1 (Sol: 88.8%)
Not included
Max reasoning effort and ultra mode, Sol only
01

What Is GPT-5.6 Terra?

GPT-5.6 Terra went generally available on July 9, 2026, alongside Sol and Luna. Under OpenAI's new naming scheme the number is the generation and the name is the tier, and Terra is the middle one: below Sol, the flagship, above Luna, the budget tier. The API identifier is gpt-5.6-terra.

OpenAI's own positioning is unusually direct: Terra delivers roughly GPT-5.5 performance at about half the price. GPT-5.5 was the flagship until July 9, 2026; getting its class of capability at $2.5 in / $15 out per MTok instead of $5 in / $30 out per MTok is the headline of this tier, and the reason Terra, not Sol, is the sane starting point for most work. Where it sits in the full lineup lives at the models hub.

02

Specs: The Verified Data

SpecGPT-5.6 Terra
VendorOpenAI
API identifiergpt-5.6-terra
Generally availableJuly 9, 2026
Price$2.5 in / $15 out per MTok
TierWorkhorse (middle of the GPT-5.6 family)
Terminal-Bench 2.187.4% (OpenAI launch materials)
CodexSelectable in Codex (current generation)

Data verified July 18, 2026 against OpenAI's published pricing and launch materials. And in family context:

GPT-5.6 SolGPT-5.6 TerraGPT-5.6 Luna
RoleFlagshipWorkhorseBudget / fast
Price$5 in / $30 out per MTok$2.5 in / $15 out per MTok$1 in / $6 out per MTok
Terminal-Bench 2.188.8%87.4%84.7%
Max reasoning effortYesNoNo
Ultra modeYesNoNo

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03

The Price-Performance Case for Terra

Here is the arithmetic that makes Terra the default. On Terminal-Bench 2.1, the benchmark OpenAI led its launch with, Sol scores 88.8% and Terra scores 87.4%. That is a 1.4-point gap. The price gap is 2x: $5 vs $2.5 per million input tokens, $30 vs $15 on output. Paying double for 1.4 points only makes sense when those points are the difference between a task succeeding and failing, which for routine implementation, tests, and refactors they usually are not.

The same logic applies against the previous generation. GPT-5.5 is still selectable in Codex at $5 in / $30 out per MTok, the same price as Sol, for performance OpenAI itself says Terra roughly matches at half the cost. If you are still defaulting to GPT-5.5 out of habit, Terra is the upgrade that also cuts your bill; the full picking logic, including reasoning effort, is in the best Codex model guide, and the budgeting side in Codex usage limits.

04

What Terra Lacks, and When to Escalate to Sol

Terra's tradeoff is a short list, but a real one: no max reasoning effort and no ultra mode. Both are exclusive to Sol. Max reasoning effort is the family's highest thinking budget; ultra mode fans a task out to parallel subagents, and OpenAI's launch materials credit it with lifting Sol's Terminal-Bench 2.1 score further still. You cannot buy either on Terra at any price.

So the escalation rule is about failure cost, not prestige. Escalate to Sol when a failed Terra run plus a re-run would cost more than one Sol run succeeding: genuinely hard cross-cutting problems, long autonomous sessions where a mid-run derailment is expensive, and terminal-heavy chains where Sol's lead is measured. For everything else, pocket the 2x difference.

05

When Luna Suffices

Stay on Terra

Routine implementation, tests, refactors, most Codex sessions. 87.4% on Terminal-Bench 2.1 at $2.5 in / $15 out per MTok is the family's best capability per dollar.

Drop to Luna

Mechanical, high-volume work: lint fixes, renames, formulaic edits. $1 in / $6 out per MTok is 2.5x cheaper than Terra, and 84.7% is plenty for tasks with no real reasoning in them.

Escalate to Sol

Hard problems, long runs, terminal-heavy chains, or anything wanting max reasoning effort or ultra mode. $5 in / $30 out per MTok, and worth it exactly when failure is expensive.

Luna is 2.7 points behind Terra on Terminal-Bench 2.1, a bigger drop than Sol-to-Terra, so treat it as a tier for tasks you could almost script, not a cheaper Terra. In practice the tiers work best as a routing decision made per task, not per project: AIDEN runs your Codex CLI on a kanban board where each story picks its own model, so the mechanical cards go to Luna, the default work to Terra, and the hard stories to Sol, with each result on its own branch telling you whether the routing was right.

FAQ

What is GPT-5.6 Terra?
GPT-5.6 Terra is the workhorse tier of OpenAI's GPT-5.6 family, generally available since July 9, 2026 and selectable in Codex. OpenAI positions it as roughly GPT-5.5 performance at about half the price: $2.5 in / $15 out per MTok versus GPT-5.5's $5 in / $30 out per MTok. It sits between Sol (the flagship) and Luna (the budget tier) and scores 87.4% on Terminal-Bench 2.1.
Is Terra as good as GPT-5.6 Sol?
Close, at half the price. On Terminal-Bench 2.1 Terra scores 87.4% to Sol's 88.8%, a 1.4-point gap against a 2x price gap ($2.5 in / $15 out per MTok vs $5 in / $30 out per MTok). What Terra genuinely lacks is Sol's two exclusives: max reasoning effort and ultra mode's parallel subagents. Neither is available on Terra at any price.
When should I escalate from Terra to Sol?
When the task is hard enough that a failed Terra run plus a re-run would cost more than one Sol run succeeding: hard cross-cutting problems, long agentic sessions, and terminal-heavy work where Sol's benchmark lead is real. Also whenever you want max reasoning effort or ultra mode, which only Sol has. For routine implementation, Terra's 87.4% is close enough that the 2x premium rarely pays.
When is Luna enough instead of Terra?
For mechanical, high-volume work: lint fixes, renames, small formulaic edits. Luna costs $1 in / $6 out per MTok, which is 2.5x cheaper than Terra on both sides of the meter, but it is the weakest 5.6 tier on agentic benchmarks (84.7% on Terminal-Bench 2.1, 2.7 points behind Terra). If the task has any real reasoning in it, Terra is the safer spend.
Is Terra better than GPT-5.5?
On the published data it is roughly equal for half the price: OpenAI positions Terra as GPT-5.5-class performance at $2.5 in / $15 out per MTok, while GPT-5.5 still costs $5 in / $30 out per MTok. GPT-5.5 remains selectable in Codex for teams with tuned workflows, but for new work Terra is the obvious starting point.

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