{
  "id": 3869008,
  "title": "GPT-5.6 Sol vs Terra vs Luna: A Cost-Aware Router in Python",
  "url": "https://urgent.news/2026/08/28/gpt-5-6-sol-vs-terra-vs-luna-a-cost-aware-router-in-python",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-28T02:23:57.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/postal6666/gpt-56-sol-vs-terra-vs-luna-a-cost-aware-router-in-python-7eg"
  },
  "original_language": "en",
  "account": "When considering which model from OpenAI's GPT-5.6 family to use for a specific task, it's not merely a matter of determining the \"best\" model. The more pertinent question is which part of the workflow genuinely requires the Sol tier. OpenAI categorizes the GPT-5.6 models into three tiers, each with distinct pricing structures. The Model API ID Input and Output token pricing for these tiers are as follows: Sol at $4.00 for 1M tokens input/output, Terra at $2.00 for 1M tokens input and $12.00 for 1M tokens output, and Luna at $0.20 for 1M tokens input and $1.20 for 1M tokens output. These rates can change, so it's advisable to manage them in configuration rather than hardcoding them into application code. The price difference between these tiers is substantial enough to influence architectural decisions. For instance, a task that generates 8,000 input tokens and outputs 1,500 tokens would cost approximately $0.062 using Sol, $0.034 with Terra, and $0.0034 with Luna. Over 100,000 tasks, these costs escalate to around $6,200 for Sol, $3,400 for Terra, and $340 for Luna. However, the real challenge lies in deciding which tasks warrant the more expensive Sol tier. It's not advisable to route based solely on prompt length, as a succinct request might conceal a complex problem, whereas a lengthy document may only necessitate simple extraction. A straightforward, easily auditable routing mechanism can be established by defining a task class that includes attributes for the task's kind, ambiguity level, and whether it requires a final review. Models are then chosen based on these attributes. High-consequence tasks or those involving ambiguous, debugging, planning, or multi-step analysis are routed to Sol, while tasks with high ambiguity or requiring debugging are handled by Terra. All other tasks default to Luna. This approach ensures that tasks requiring reliable, high-quality results are allocated to the more expensive, yet more capable models, while simpler tasks are handled by the more cost-effective Luna. Implementing this routing strategy in a real-world application involves creating a client using OpenAI's API, defining a Task class, and using the choose_model function to select the appropriate model based on the task's attributes. Once the task is assigned a model, it can be processed using the OpenAI responses API. This model selection logic can be integrated into any pipeline where GPT-5.6 models are employed, allowing for efficient and cost-aware processing of tasks.",
  "summary": "The useful question about GPT-5.6 is not “Which model is best?” It is “Which part of this workflow actually needs Sol?” OpenAI now positions the family in three tiers: Model API ID Input / 1M tokens Output / 1M tokens Sensible default Sol gpt-5.6-sol $4.00 $20.00 Ambiguous, high-consequence reasoning Terra gpt-5.6-terra $2.00 $12.00 Everyday production work Luna gpt-5.6-luna $0.20 $1.20…",
  "key_points": [
    "GPT-5.6 models have Sol, Terra, and Luna tiers with different pricing.",
    "Sol tier costs $4.00 per 1M input/output tokens, Terra $2.00/$12.00, Luna $0.20/$1.20.",
    "Task attributes like ambiguity and review need determine model selection."
  ],
  "editors_take": "This development enables developers to optimize GPT-5.6 model usage by allocating tasks to different tiers based on their complexity and requirements, balancing cost and quality.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}