An AI-ready project brief: the template that stops endless prompting
An AI project brief connects outcomes, scope, sources, constraints, and validation. Use this template before assigning work to an agent.
6 min read
The GPT-5.6 family has three clearly positioned models: Sol (hard work), Terra (capability versus cost), and Luna (volume and price). GPT-6 Astra now sits at the top of OpenAI's lineup; this page remains useful for choosing the most economical tier within the 5.6 family.
An agent reviewing architecture does not need the same compute as an agent labelling tickets. A workflow run thousands of times a day can lose its economics if every step uses the flagship.
The real question: when should you use Sol, Terra, or Luna?
GPT-5.6 Luna
Speed · High volume
Triage, classification, extraction
GPT-5.6 Terra
Balance · Everyday work
Review, synthesis, AI workflows
GPT-5.6 Sol
Capability · Hard missions
Debug, research, open-ended agents
The essential differences
OpenAI's current model catalog places GPT-6 Astra above the 5.6 family. Within that family, Sol remains the most capable choice for complex reasoning and coding, Terra balances capability and cost, and Luna targets speed and high volume.
That does not make Luna “bad”, Terra “average”, and Sol “good”. They optimize different constraints.
GPT-5.6 Sol
Maximum capability within the 5.6 family. Hard reasoning, difficult coding, research, and open-ended agents.
GPT-5.6 Terra
Balance. Analysis, review, daily tasks, AI workflows. OpenAI places it roughly at the “mini” level of previous GPT generations.
GPT-5.6 Luna
Speed and cost. Classification, triage, extraction, large pipelines. First-pass work, then escalate the hard cases.
Not one model everywhere
The best model is no longer the one to use everywhere. A reviewer does not need the same compute as a debugging agent.
Sol: when quality comes first
Spend more compute when a mistake or shallow reasoning costs more than the model call.
Natural fits: complex codebases, hard bugs, architecture, multi-step research, large document sets, several tools, an autonomous agent on an open mission, a demanding review.
Example: “Find the regression, propose a fix, change the project, and confirm tests pass.” The number of steps is unpredictable. Saving a few cents can be a false economy if a lighter model multiplies retries.
Terra: often the most interesting model for agents
Terra is for workloads that must balance intelligence and cost. As of September 7, 2026, official API pricing is $2 per million input tokens, $12 output, and $0.20 cached input.
It is often enough for tickets, content review, structured generation, synthesis, docs, human-assisted agents, transforms, ordinary code, and multi-step AI workflows.
A bounded reviewer (receive, check, find issues, suggest fixes) needs intelligence inside a defined perimeter. Terra is often the better economic choice than Sol.
Luna: do not underestimate it
As of September 7, 2026, Luna costs $0.20 per million input tokens and $1.20 output. Terra costs $2 / $12, compared with $4 / $20 for Sol. The gap remains substantial.
| Model | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| GPT-5.6 Sol | $4.00 | $20.00 |
| GPT-5.6 Terra | $2.00 | $12.00 |
| GPT-5.6 Luna | $0.20 | $1.20 |
One hundred thousand pieces of feedback to classify is not where you want Sol. Luna does the first pass; Sol only steps in when the case requires it.
Rates above are standard API pricing (short context). They change: check the official page before you commit spend.
Stop picking a single model
A system has no obligation to use the same model for every task.
One model
A routed family
Sol on every step
Luna / Terra / Sol by mission
Simple to configure
Cheaper at volume
Uniform quality
Quality where it matters
Flagship latency everywhere
Fast replies on triage
Example team: triage → Luna, reviewer → Terra, developer → Sol. Cheaper, faster, and still high quality if routing follows real difficulty.
| Agent type | Model | Why |
|---|---|---|
| Triage agent | Luna | Speed and cost |
| Classification | Luna | Structured task |
| Extraction | Luna | Little reasoning needed |
| Docs assistant | Terra | Solid compromise |
| Writing agent | Terra | Enough quality |
| Reviewer | Terra ou Sol | Depends on difficulty |
| Everyday coding | Terra | Balance |
| Hard debug | Sol | Stronger reasoning |
| Multi-step research | Sol | High ambiguity |
| Open-ended agent | Sol | Planning and adaptation |
The table is a starting point, not a law. Prompt, tools, context, input size, and the quality bar matter as much as the agent’s role.
Must an autonomous agent always use Sol?
No. Separate the agent type from the difficulty of its missions.
“Check each new ticket, pick the team, apply the label”: the agent decides, the task is bounded. Luna or Terra can be enough.
“Analyse this codebase, find why performance dropped, and propose a plan”: even a single mission can justify Sol.
The metric is not “is this agent autonomous?” — it is how much intelligence does this mission actually need? Same principle as choosing automation, workflow, or agent.
Several models in one workflow
Feedback → Luna (classify) → Terra (analyse) → simple cases done / hard cases → Sol.
Development: Terra (first implementation) → tests → Luna (summarise) → Sol (hard review).
Or: Sol (plan) → Terra (subtasks) → Sol (final review).
There is no perfect combination. It depends on the workflow — see also model fusion in multi-agent pipelines.
Do not read cost in isolation
A cheaper model can cost more if it produces more errors, retries, tokens, tool calls, or human fixes. Sol everywhere wastes budget on trivial work.
Useful metric: cost per correctly finished task. One Sol run at $0.20 with no correction can beat five Luna runs at $0.02 plus a human intervention.
The question is not “which model is cheapest?” It is “which is the cheapest model that reaches the quality this task needs?”
Speed
Luna
Fastest in the family
Terra
In the middle
Sol
Capability first, Fast mode in the API
Latency matters most in interactive loops. A few extra seconds are immediately visible after a user click; they often matter less than final quality in an agent mission that already runs for several minutes.
ChatGPT, Codex, and the API
The three models are not exposed the same way everywhere. The OpenAI API offers Sol, Terra, and Luna. Codex and ChatGPT Work expose them on eligible plans. In standard ChatGPT conversations, Terra and Luna are not always selectable as distinct models.
A recommendation system must consider what is actually available (product, plan, channel, provider) — not only the theoretically best model. For a cross-vendor coding comparison: GPT-6 Astra vs Claude Fable 5.1 vs Gemini 3.5 Flash vs Cursor. For Anthropic’s top tier: Fable 5.1 vs Opus 5.
Why this matters for agents
An agentic run chains analysis, tools, and retries. A small per-call gap becomes huge across thousands of missions. Hence routing: simple → Luna, medium → Terra, hard → Sol, with escalation if a level fails.
Routing can use complexity, budget, latency, role, context size, tools, and success history.
Stellary
In Stellary, agents are workspace members: role, tools, autonomy, missions. The platform tracks activity and cost. Distinguishing Sol, Terra, and Luna is a natural fit — a triage agent should not pay Sol by default.
That does not mean a fixed model per agent. A reviewer can stay on Terra 90% of the time and switch to Sol for an unusually hard review. The interesting future: each mission uses the level it needs. For control: manage AI agents without losing control.
Starting grid
Choose Luna
Short task, fairly clear rules, high volume, cost-critical, latency-sensitive, errors easy to detect or escalate.
Choose Terra
Real analysis, Sol would be oversized, regular use, quality/cost compromise, several AI calls in the workflow.
Choose Sol
Hard problem, costly mistakes, reasoning at the centre, exploration, several tools, complex coding or research, open mission.
Route
Volume on Luna, daily work on Terra, hard problems on Sol, escalate if needed. A single model is still fine on a simple system.
FAQ
What is the difference between GPT-5.6 Sol, Terra, and Luna?
Sol is the most capable model in the 5.6 family. Terra balances capability and cost. Luna prioritizes speed, volume, and low cost. GPT-6 Astra now sits at the top of OpenAI's lineup.
Which GPT-5.6 model should you use for coding?
Within the 5.6 family, Sol is the better starting point for complex coding and Terra for everyday development. For the most demanding tasks, also evaluate GPT-6 Astra.
Which OpenAI model should an autonomous agent use?
Sol is usually the best starting point for genuinely open-ended missions. A simpler agent can still run well on Terra or Luna.
Is GPT-5.6 Luna suitable for AI agents?
Yes, especially for triage, classification, extraction, and other simple, high-frequency workloads.
Is GPT-5.6 Terra weaker than Sol?
Terra is a different compromise. Sol pushes capability further; Terra aims for strong results at a lower cost.
How much does GPT-5.6 Sol cost?
As of September 7, 2026, standard API pricing is $4 per million input tokens and $20 per million output tokens. Check the official page before committing spend.
How much do GPT-5.6 Terra and Luna cost?
Since 30 July 2026, Terra is $2 per million input and $12 output. Luna is $0.20 input and $1.20 output.
Can you use Sol, Terra, and Luna in the OpenAI API?
Yes. All three are available in the OpenAI API. Availability in ChatGPT and Codex depends on the plan.
