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GPT-5.6 Sol, Terra and Luna: what each tier costs, and which one to point at your repo
Run Terra as the default in Codex, escalate to Sol on hard agentic work, keep Luna for mechanical edits. OpenAI's GPT-5.6 family reached general availability on July 9, 2026 across ChatGPT, Codex and the API, split into three tiers that differ only in price and reasoning ceiling — Sol at $5 input / $30 output per million tokens, Terra at $2/$12, Luna at $0.20/$1.20. All three share one context window (1,050,000 tokens), one output cap (128,000) and one knowledge cutoff (February 16, 2026). Against Claude, Sol ties Opus 5 on input, costs 20% more on output, and undercuts Fable 5 by roughly half. But the routing decision is not the sticker price — it is which benchmark family your work resembles, because OpenAI's own numbers put Sol well ahead on terminal-style agent tasks and well behind Claude on SWE-Bench Pro.
The three tiers and what changes between them
This is not a size ladder in the usual sense: the three share context specs, and what moves is
cost per token and how much reasoning the model may spend. API ids are exactly
gpt-5.6-sol, gpt-5.6-terra and gpt-5.6-luna; the bare
gpt-5.6 alias resolves to Sol, so an unqualified id bills at the top rate.
Updated August 2, 2026: two of these prices were cut. When this post first ran on July 30, Terra was $2.50/$15 and Luna was $1/$6. OpenAI has since moved Terra to $2/$12 and Luna to $0.20/$1.20 — a five-fold cut on Luna, which is now cheaper than most open-weight models. Sol is unchanged. Every figure below, and the Codex credit rates further down, was re-read from OpenAI's rate card on August 2; the per-session table has been recomputed rather than relabelled.
| GPT-5.6 Sol | GPT-5.6 Terra | GPT-5.6 Luna | Claude Opus 5 | Claude Fable 5 | |
|---|---|---|---|---|---|
| API price (per M tokens) | $5 / $30 | $2 / $12 | $0.20 / $1.20 | $5 / $25 | $10 / $50 |
| Cache read | $0.50 | $0.20 | $0.02 | $0.50 | $1.00 |
| Cache write | $6.25 (1.25x) | $2.50 (1.25x) | $0.25 (1.25x) | $6.25 (5-min) | $12.50 (5-min) |
| Long-context rate (>272K in) | $10 / $45 | $4 / $18 | $0.40 / $1.80 | n/a | n/a |
| Priority tier | $10 / $60 | $4 / $24 | Not listed | n/a | n/a |
| Context / max output | 1.05M / 128k | 1.05M / 128k | 1.05M / 128k | 1M / 128k | 1M / 128k |
| Knowledge cutoff | Feb 16, 2026 | Feb 16, 2026 | Feb 16, 2026 | May 2026 | Jan 2026 |
| Top reasoning effort | ultra | ultra | max | Anthropic effort scale | |
| Terminal-Bench 2.1 (vendor-reported) | 88.8% | 87.4% | 84.7% | Not in table | 83.1% |
| SWE-Bench Pro (vendor-reported) | 64.6% | 63.4% | 62.7% | Not in table | 80.0% |
| Backgrind | Model-agnostic. PTY-wraps your own Codex CLI or Claude Code; run one tier per tab and compare the diffs. | ||||
One provenance note before anyone quotes that table: the benchmark rows are OpenAI's own, and they went up on July 9, two weeks before Claude Opus 5 shipped, so the Claude column they published was Fable 5 and the then-current Opus. No vendor has published a head-to-head GPT-5.6 vs Opus 5 table, and the Opus 5 figures in secondary coverage are read off chart images. Treat any Sol-vs-Opus-5 benchmark claim as unsettled.
The two surcharges the headline price hides
The 272K cliff. Any request whose prompt exceeds 272,000 input tokens is billed at 2x input and 1.5x output for the entire request — not just the overflow. Sol jumps to $10/$45, Terra to $4/$18, Luna to $0.40/$1.80. Tellingly, Codex CLI's shipped catalog caps the window it uses at 272,000 tokens, exactly the surcharge boundary, so the CLI will not walk you off that cliff by accident. Drive the API directly and your context-assembly code has to enforce the line.
Cache writes now cost more than input. Starting with 5.6, writing to the prompt cache bills at 1.25x the uncached input rate — a Sol cache write is $6.25 per million against $5.00 for the same tokens uncached. Reads keep the 90% discount ($0.50 on Sol), and the family adds explicit cache breakpoints plus a 30-minute minimum cache life. Work the arithmetic before panicking: the write premium is 0.25x input ($1.25 per million on Sol) while every hit saves 0.9x ($4.50), so a single read back repays the surcharge several times over. What actually loses money is caching a prefix you never read back inside the cache window — the one-shot script that writes a system prompt to cache and exits. Long-lived agent sessions are unaffected. The Claude side of this math is in our Opus 5 vs Fable 5 breakdown; both rate cards sit in the pricing tracker.
What a session actually costs
Coding agents run roughly 20:1 input to output — reading files and tool results dwarfs the diffs they write. At 2M input / 100k output, held under the 272K per-request line:
| Model | Uncached | 90% cache reads |
|---|---|---|
| GPT-5.6 Sol | $13.00 | $4.90 |
| GPT-5.6 Terra | $5.20 | $1.96 |
| GPT-5.6 Luna | $0.52 | $0.20 |
| Claude Opus 5 | $12.50 | $4.40 |
| Claude Fable 5 | $25.00 | $8.80 |
Sol and Opus 5 look like a coin flip there — about 4% apart. They are not, and the reason is tokenization. Anthropic states that Claude 4.7-and-later models use a newer tokenizer producing approximately 30% more tokens for the same text, so $5/MTok on Claude and $5/MTok on OpenAI do not buy the same amount of your repo. Apply that correction and the same session on Opus 5 lands nearer $16 than $12.50 — ahead of Sol rather than behind it, though the gap narrows on output-heavy work where Opus 5's cheaper output rate reasserts itself. To run your own mix, the agent cost calculator takes arbitrary token counts.
How this lands in Codex CLI
gpt-5.6-sol is Codex CLI's default model, and the CLI must be on 0.144.0 or newer —
the shipped catalog sets minimal_client_version: "0.144.0" on all three 5.6 entries,
and that release went out the same day as GA. The same catalog retires gpt-5.4 and gpt-5.4-mini
with forced upgrade paths to Terra and Luna. Selection works four ways:
codex --model gpt-5.6-solfor an interactive sessioncodex exec -m gpt-5.6-luna "Your task"for headless runs- the interactive
/modelpicker, which renders1. gpt-5.6-sol (default) model = "gpt-5.6"in~/.codex/config.toml— remembering that the bare alias means Sol
Installation is unchanged — npm install -g @openai/codex, or the platform installer
script OpenAI publishes; our Codex CLI install walkthrough
covers both routes and the auth paths.
Check one thing on your own install rather than take it from docs: Sol's default reasoning
effort is genuinely ambiguous. OpenAI's Codex docs show "medium (default)", the shipped
catalog sets default_reasoning_level: "low" for Sol (Terra and Luna are medium), and
the launch post nudges users to "try starting lower." Run /model and read what your
binary says. The API ladder is none | minimal | low | medium | high | xhigh | max,
defaulting to medium; ultra is Codex- and ChatGPT-only, coordinates parallel
subagents, works on Sol and Terra but not Luna, and needs Plus or higher. No multiplier is
published for it, and the 0.144.0 notes warn that "high multi-agent concurrency could increase
usage quickly" — so any per-task cost estimate for ultra is guesswork.
Credits, not dollars, if you are on a ChatGPT plan
Codex on a ChatGPT login does not bill API dollars — it burns credits, at 125 per million Sol
input tokens (12.5 cached, 750 output), 50/5/300 for Terra and 5/0.5/30 for Luna. OpenAI
says a GPT-5.6 message averages 5-40 credits, and that Plus and Business include 10-100 Sol
messages per five-hour window (Terra 25-200, Luna 250-2,000), with Pro 5x at 50-500, 125-1,000
and 1,250-10,000.
/status shows what is left mid-session. Those ratios divide cleanly at $0.04 per
credit across all three tiers, but OpenAI publishes no credit-to-dollar rate we could reach —
treat it as an inference. Signing in with an API key instead means you "pay only for the tokens
Codex uses, based on API pricing." On gating: Free and Go get Terra, Plus and above choose among
all three. The CLI catalog does list free and go under Sol's available_in_plans, but
that likely describes visibility rather than entitlement — do not plan around free-tier Sol.
The routing rule, and the benchmark split behind it
OpenAI's own eval table does not tell one story. On Terminal-Bench 2.1, Sol scores 88.8%
(91.9% at ultra) against Fable 5's 83.1%. On SWE-Bench Pro, Sol scores 64.6% against Fable 5's
80.0%. Both are from the same vendor page. Terminal-Bench rewards driving a shell,
chaining tools and recovering from errors; SWE-Bench Pro rewards landing a correct patch in a
large unfamiliar codebase. GPT-5.6 is tuned hard for the first shape — consistent with its
headline API feature, Programmatic Tool Calling, where the model writes code that runs in an
isolated hosted sandbox without network access, coordinating tools and processing intermediate
results there instead of round-tripping every result through the context. All three 5.6 models are flagged tool_mode: "code_mode_only" in
Codex's catalog.
Independent measurement broadly agrees. Artificial Analysis put Sol at max effort at 59 on their Intelligence Index v4.1, one point below Fable 5, at $1.04 per index task against $0.55 for Terra and $0.21 for Luna — figures computed at the pre-August rate card, so both now run lower — while Sol leads their Coding Agent Index at 80. Secondary reporting of that leaderboard after Opus 5 shipped puts Opus 5 on top at roughly 61; we could not confirm those post-Opus-5 numbers against a primary table, so they are the shakiest figures here.
One caution on Terra. A vendor-run harness (CodeRabbit, not a standard benchmark) found Sol at a 63.7% pass rate using about 20,968 output tokens per task, against Terra at 40.7% using about 55,594 — roughly 2.7x the tokens for a substantially lower pass rate on long jobs. Half the price per token is not half the price per fix if the cheap tier thrashes.
So: Luna for mechanical, well-specified edits and batch work, noting its weak long-context retrieval (OpenAI-reported MRCR v2 8-needle at 512K-1M: 41.3%, against Sol's 73.8%). Terra as the default for bounded tasks with a clear finish line. Sol for terminal-shaped autonomous work and long sessions. And when the task is "land a correct patch in a large codebase I do not know," Claude's SWE-Bench Pro lead is worth running both — which our Claude Code vs Codex comparison covers in workflow terms.
Frequently asked questions
What is the difference between GPT-5.6 Sol, Terra and Luna?
Price and reasoning ceiling, not context. All three share a 1,050,000-token context window, 128,000 max output tokens and a February 16, 2026 knowledge cutoff. Sol is the frontier tier at $5 input / $30 output per million tokens, Terra runs at $2/$12 — 40% of Sol on input — and Luna is the cheap tier at $0.20/$1.20, a twenty-fifth of Sol. Sol and Terra support the ultra reasoning effort in Codex and ChatGPT; Luna caps at max. The bare gpt-5.6 alias routes to Sol.
Is GPT-5.6 Sol cheaper than Claude Opus 5?
On input they tie at $5 per million; on output Sol is $30 against Opus 5's $25, so Sol runs about 4% more expensive at a typical 20:1 agent mix. But Anthropic notes that Claude 4.7-and-later models use a newer tokenizer producing roughly 30% more tokens for the same text, so the same repo costs more Claude tokens than OpenAI tokens. Adjusted for that, Opus 5 lands above Sol. Measure cost per solved task on your own code rather than trusting either sticker.
Which Codex CLI version do I need for GPT-5.6?
Version 0.144.0 or newer. The CLI's shipped model catalog sets minimal_client_version to 0.144.0 on all three GPT-5.6 models, and rust-v0.144.0 was published on July 9, 2026, the same day as GA. The same catalog retires gpt-5.4 and gpt-5.4-mini with forced upgrade paths to Terra and Luna respectively. If GPT-5.6 does not appear in your /model picker, upgrade before assuming your plan excludes it.
Does GPT-5.6 have hidden pricing beyond the headline rates?
Two multipliers worth knowing. Any request whose prompt exceeds 272,000 input tokens is billed at 2x input and 1.5x output for the entire request, which puts Sol at $10/$45 effective. And starting with 5.6, cache writes bill at 1.25x the uncached input rate — $6.25 per million on Sol — while cache reads keep the 90% discount. Priority-tier processing is a separate rate card again, at $10/$60 for Sol.
Where Backgrind fits
Backgrind is not a model and not an agent — it is a desktop overlay for macOS and Windows that PTY-wraps the CLI you already run, Codex included, alongside Claude Code, Cursor and OpenCode. It keeps that session in an always-on-top window over other apps — including games run in borderless-fullscreen, not exclusive fullscreen — and pings you when the agent needs a decision or finishes. In BYO-CLI mode your agent's content never touches our servers; Live mode (Plus and Pro) lets you answer those prompts from a phone.
For this question specifically: the honest way to settle Sol vs Terra vs Opus 5 is to run the same task in parallel tabs and read the diffs, not the leaderboards. If you would rather have that judged for you, the Fusion council puts your own CLIs on one hard question in parallel and returns a single verdict, pinging you only when they split or come back low-confidence.
Sources
GA date, tier lineup, plan gating and all benchmark tables (vendor-reported): OpenAI — GPT-5.6. Preview restrictions: Previewing GPT-5.6 Sol. List prices, cache-write multiplier and priority-tier rates: OpenAI API pricing. Context specs, endpoints and the 272K long-context surcharge: gpt-5.6-sol model page. Codex model selection and CLI defaults: Codex models; credit rate card and included message counts: Codex pricing. Minimum client version, tool_mode flags, retirements and default reasoning levels: openai/codex (README, codex-rs/models-manager/models.json, releases). Claude prices, cache multipliers and the tokenizer note: Anthropic — model pricing and Introducing Claude Opus 5. Independent index scores and per-task costs: Artificial Analysis — GPT-5.6 has landed. Single-harness pass-rate comparison: CodeRabbit — Sol and Terra benchmark.