Tabbit
ResourcesBlogModels
Tabbit LogoTabbit

Tabbit — The AI Browser that Works for You

Topics

  • AI Browser Resources
  • Agentic Browser Resources
  • Browser Downloads and Install Guides
  • Browser Comparisons
  • AI Browser Alternatives
  • Browser Productivity Resources

Popular Guides

  • AI Browser
  • Agentic Browser Download
  • Best AI Browser 2026: Top 9 Tested & Ranked
  • AI Browser Download
  • Free AI Browser
  • Best AI Browser 2026
  • AI Browser Comparison 2026
  • AI Browser for Windows
  • AI Browser for Mac
  • Chrome Alternative 2026

Events

  • Tabbit Skill Competition
  • KPOP SBTI Fandom Personality Test
  • Tabbit Campus Creator Program
  • fifi's Picks: AI Skills for Research Papers
  • User Survey

About

  • Tabbit Blog
  • Press & Media
English
简体中文English
Prompts and workflows

GPT-5.6 Terra · workflow

Sol-Advisor: A Codex Workflow with Sol for Orchestration, Terra for Complex Execution, and Luna for Routine Execution

The Sol-Advisor case assigns coordination and review to Sol, complex execution to Terra, and routine work to Luna in an auditable multi-model pipeline.

Source not verifiedGPT-5.6 Terra client or API; confirm the live model ID, tools, permissions, and version before execution.

Prerequisites and inputs

  • task goal
  • source or reference material
  • runtime constraints
  • acceptance criteria

One-sentence takeaway

The core concept of Sol-Advisor is using Sol High for orchestration, Luna Max for routine task execution, Terra Max for complex execution, and a fresh Sol instance for review—concentrating high-capability models strictly on planning and acceptance.

Suitable use cases

  • Suitable tasks: Engineering tasks in Codex that can be broken down and require a plan-execute-review closed loop.

  • Unsuitable tasks: Small one-off tasks, tasks without verifiable acceptance criteria, and tasks involving destructive write operations without human confirmation.

  • Applicable model versions: GPT-5.6 Sol High, GPT-5.6 Luna Max, GPT-5.6 Terra Max.

  • Applicable clients, agents, or APIs: The original post refers to a Codex plugin/workflow; installation commands do not appear in the publicly visible page text.

  • Recommended reasoning tiers and parameters: Sol High for orchestration/review, Luna Max for routine tasks, and Terra Max for complex tasks. This is the role division proposed by the author, not a benchmarked optimum.

Copy-ready content

Sol-Advisor Role Breakdown:
1. GPT-5.6 Sol High acts as the orchestrator: breaks down tasks, dispatches subtasks, and maintains overall objectives.
2. GPT-5.6 Luna Max acts as the routine task executor: handles well-defined, repetitive, and low-risk work.
3. GPT-5.6 Terra Max acts as the complex task executor: handles work requiring deeper reasoning.
4. A fresh GPT-5.6 Sol instance acts as the reviewer: inspects results, tests, and validates whether original objectives are met.

How to use

  1. Have Sol High decompose the objective into independently verifiable subtasks.

  2. Route clear, repetitive subtasks to Luna Max; route complex implementations or critical judgments to Terra Max.

  3. Collect execution artifacts, test results, and unresolved issues.

  4. Have a fresh Sol instance review diffs, tests, and acceptance criteria; if checks fail, route back to the executor for fixes.

  5. Require human confirmation for external side effects, deletions, and deployment actions.

Visibility and boundaries in the original post

  • The original post explicitly discloses the role division, but the installation instructions for the "free open-source Codex plugin" are not detailed in the currently visible text; do not invent installation commands or repository URLs.

  • The original post does not provide control groups, failure rates, context usage, quota consumption, or cost figures under identical tasks; therefore, this is a workflow rather than a reproducible benchmark.

  • The statement that "Luna Max on high reasoning roughly equals Sol on medium reasoning at roughly one-sixth the cost" comes from another experiential post cited by the author; this is an individual claim and should not be taken as a performance guarantee for Terra.

Source and dates

X · Source date: 2026-08-02 · Edited: 2026-09-20

Read the original source
Variable checklist

No required variables

Related prompts

Generating Entrance Animations and Layout Variations in Framer Agent with GPT-5.6 TerraGPT-5.6 Terra API Model Parameters and Tool ConfigurationGPT-5.6 Terra Frontend Interaction Prototype Prompts and Validation WorkflowGPT-5.6 Terra Long-Context Cost Thresholds and Routing Workflow

Related reviews

GPT-5.6 Terra: SonarSource's Retest of Code Quality and Security on 4,444 Java TasksGPT-5.6 Terra System Card: Safety Guardrails and Agent BoundariesOfficial OpenAI GPT-5.6 Terra Benchmarks, Pricing, and Task BoundariesGPT-5.6 Terra: Artificial Analysis Intelligence, Cost, and Coding Agent Indices

Read the full analysis

Overview · English

GPT-5.6 Terra: What It Is, Access, and Where It Fits

A sourced GPT-5.6 Terra overview covering API limits, Sol and Luna differences, access surfaces, cost boundaries, and practical risks.

GPT-5.6 Terra

Use GPT-5.6 Terra in Tabbit

Run this guide in the environment listed above. Downloading does not transfer the template or establish model availability for your account.