The useful answer to GPT-5.6 Sol vs Terra vs Luna is not “pick the smartest model.” Sol, Terra, and Luna occupy different positions on the capability, latency, and price curve. For an AI agent, the best design usually routes work between them while keeping the tools, browser state, evidence, and success criteria consistent. Sol is the choice for ambiguity and difficult judgment. Terra is the default for repeatable knowledge and tool workflows. Luna is the economical worker for classification, cl

GPT-5.6 Sol vs Terra vs Luna at a glance

The output-price spread is the first economic signal. Sol output costs five times Luna output. But a lower price per token does not guarantee a lower price per successful workflow.

Pro Tip: Use the model comparison table to design an escalation ladder, not a permanent assignment. A Terra task should be able to move to Sol when evidence conflicts or the page state is ambiguous.

Choose GPT-5.6 Sol when failure is expensive

Sol belongs at the parts of a workflow where interpretation matters more than throughput.

Good Sol tasks include:

• Turning an underspecified business request into a measurable task contract.
• Planning a multi-source investigation with conflicting evidence.
• Deciding whether an unexpected page represents a redesign, an account problem, or bad data.
• Supervising multiple workers and reconciling their results.
• Reviewing a proposed action that could affect customers, money, permissions, or production systems.
• Handling difficult coding and debugging work around an agent failure.

Sol is also the best baseline model when a new workflow is being developed. Starting with the strongest tier makes it easier to separate model limitations from browser, data, or orchestration failures.

That does not mean Sol should inspect every HTML fragment or format every output object. A flagship model can do routine work, but capability is not the same as economic fit.

Sol with Max or Ultra

OpenAI's max reasoning option gives the model more time to reason and revise. It is useful for a difficult individual run. Ultra coordinates multiple agents in parallel by default for demanding tasks.

Both options should be reserved for work with a clear reason to consume more compute. A broken source or wrong browser state does not improve because more agents analyze it simultaneously.

Use Max when one task requires deeper thought. Use Ultra when the work can be decomposed into independent branches with a defined reconciliation step.

Choose GPT-5.6 Terra for the normal path

Terra is the most plausible default for production agent traffic. It costs half as much as Sol on both input and output while retaining a stronger role than a lightweight cleanup model.

Typical Terra tasks include:

• Running a standard competitive research workflow.
• Comparing structured observations with a previous snapshot.
• Selecting the next browser step from a known set of states.
• Producing a cited summary from verified source records.
• Filling a predefined report or CRM schema.
• Evaluating normal validation errors before escalating.

The phrase “normal path” matters. A reliable workflow defines what normal looks like. If the page matches an expected state, the required fields are present, and the evidence passes validation, Terra can continue. If the page changes or sources conflict, the run moves to Sol or a person.

This is more robust than asking Terra to solve every possible situation or using Sol for every predictable one.

Pro Tip: Track the percentage of Terra runs escalated to Sol. A rising rate usually means the workflow, page, or task definition changed—not necessarily that Terra suddenly became worse.

Choose GPT-5.6 Luna for volume and structure

Luna is the lowest-cost GPT-5.6 tier, with $1 per million input tokens and $6 per million output tokens. Its best role is not to imitate Sol cheaply. It is to handle work whose boundaries are already clear.

Good Luna tasks include:

• Classifying a URL or job into a known workflow.
• Normalizing browser output into a fixed schema.
• Cleaning labels, units, and date formats.
• Checking whether required fields are present.
• Routing a verified observation to the correct downstream process.
• Producing short status messages from structured data.

Luna becomes a false economy when the task contains hidden ambiguity. A model that saves $0.02 but causes another browser run, a Sol escalation, and a human review did not save money.

The safest Luna tasks have deterministic validators. If a record needs a price, currency, billing period, URL, and timestamp, software can check that all five exist before accepting the result.

Why token price is not agent cost

Chat completions are easy to price: input tokens plus output tokens. Agent workflows add more variables:

Cost per completed task =
  model tokens
  + browser runtime
  + tool calls
  + retry cost
  + exception handling
  + human review time

Consider two systems processing 1,000 monitoring jobs.

System A sends every job to Sol. It has a high model bill but a low exception rate. System B sends every job to Luna. Its model bill is lower, but ambiguous pages trigger repeat runs and human reviews. Without completed-task data, the cheaper architecture cannot be identified.

A routed system attempts to keep the strengths of both:

1. Luna classifies and normalizes.
2. Terra runs the expected decision path.
3. Sol receives the exceptions.
4. A person approves sensitive actions.

Measure the cost and success rate at each stage. The goal is not to minimize Sol usage. The goal is to minimize the cost of trustworthy completion.

A routed browser-agent architecture

A browser workflow benefits from separating reasoning from execution. The website should not have to be re-automated because the team changes the model tier.

Incoming job
  → Luna: classify task and normalize known inputs
  → BrowserAct: open the live page and collect structured evidence
  → Terra: evaluate the expected state and decide the normal next step
  → Sol: resolve conflicts, novel pages, and high-impact exceptions
  → BrowserAct: execute the approved browser action or preserve a handoff

BrowserAct acts as the shared live-web and execution layer. It keeps the browser job, evidence format, and page state separate from the model selection. That makes the Sol/Terra/Luna comparison fairer and the workflow more portable.

For example, a pricing-monitoring agent can use Luna to classify known vendor URLs. BrowserAct opens each live pricing page, selects the correct market and billing interval, and returns a structured observation. Terra compares the observation with last week's record. Sol reviews only changes that are ambiguous or strategically important.

The browser work happens once. The evidence can be evaluated by whichever model is appropriate.

Decision matrix by workflow

How to benchmark the three models fairly

Use the same task definition, website state, account, evidence schema, and success criteria for each tier. Otherwise, the comparison measures different systems.

Create a test set with at least four categories:

1. Static public retrieval.
2. Interactive extraction requiring filters or tabs.
3. Stateful work inside an authorized account.
4. Exception cases with a page change, missing value, or conflicting source.

Record completed-task rate, exact data accuracy, browser retries, median time, human handoffs, and total cost. Then test the routed architecture against the best single-model baseline.

Pro Tip: Keep screenshots, URLs, timestamps, selected filters, and structured page observations outside the model transcript. This lets Sol, Terra, and Luna evaluate identical evidence without repeating the browser run.

The recommendation

If the workflow is new or difficult, start with Sol. If it is stable and repeatable, make Terra the default. Move high-volume bounded steps to Luna. Add explicit escalation rules instead of expecting one tier to handle every condition.

The durable architecture is not Sol versus Terra versus Luna. It is Sol plus Terra plus Luna, each doing the work that fits its cost and capability, connected to one reliable execution layer.

The GPT-5.6 release overview explains the broader launch. The complete GPT-5.6 browser automation guide covers the full model-data-execution architecture. To keep browser operations reusable across model tiers, explore BrowserAct agent skills.

Connect every GPT-5.6 tier to one browser execution layer →

Sources

• OpenAI — GPT-5.6
• TechCrunch — OpenAI launches GPT-5.6
• Every — GPT-5.6 Sol Vibe Check

Frequently asked questions

Which is better: GPT-5.6 Sol, Terra, or Luna?

Sol is best for the hardest and most ambiguous work, Terra is the balanced default for repeatable agent workflows, and Luna is best for fast, low-cost, bounded processing. The best production system often routes work across all three.

How much do GPT-5.6 Sol, Terra, and Luna cost?

Per million input and output tokens, Sol costs $5 and $30, Terra costs $2.50 and $15, and Luna costs $1 and $6.

Should an AI agent use Sol for every step?

No. Sol can perform routine work, but using it for classification, cleanup, and formatting can be unnecessarily expensive. Reserve Sol for planning, exceptions, difficult synthesis, and higher-consequence decisions.

Is Terra the default GPT-5.6 model for agents?

Terra is a practical default for repeatable agent work because it balances capability and price. Workflows should still escalate ambiguous cases to Sol and route bounded high-volume steps to Luna.

When should an agent use Luna?

Use Luna for classification, schema normalization, field validation, routing, and other tasks with clear boundaries and deterministic checks.

Can BrowserAct work with all three GPT-5.6 tiers?

Yes. BrowserAct is a model-neutral browser execution layer. Sol, Terra, and Luna can share the same browser workflow, live evidence, session state, and verification rules.