Pro Max Ultra Fable Sol: AI Model Names Have Escaped Containment | Tech Stackups

Skip to main content<br>Consider the kind of name string AI vendors now produce: "GPT-5.6 Sol in Codex on Pro." That sounds like one product. It can contain a generation, a model tier, a product surface, and a subscription plan. OpenAI's public GPT-5.6 page says Sol, Terra, and Luna are capability tiers, and that GPT-5.6 models will be available through the API and Codex for select partners. OpenAI's help center also uses Pro for two subscription tiers. Welcome to the problem.

Cute names can work. Haiku, Sonnet, and Opus were cute and useful. Nano Banana is cute and, against all professional instinct, memorable. The failure comes from vendors using the same words for different axes.

The four axes vendors keep mixing together​

The clean way to read any model picker is to ask which axis each word belongs to.

Naming parser<br>One model picker, four separate axes

The mess starts when one adjective is allowed to move between these boxes.

Model capability<br>The underlying model or durable model tier.<br>GPT-5.6 SolClaude Fable 5Gemini Pro

Access plan<br>What the subscription tier allows you to use.<br>ChatGPT ProClaude MaxGoogle AI Ultra

Runtime effort<br>How much work the model does for one request.<br>xhighmaxultracode

Product surface<br>The app or agent environment that runs the model.<br>CodexClaude CodeAntigravity

Those axes are independent. You can run a strong model at low effort, a cheaper model at high effort, or a coding product on an expensive plan while still using a medium model. A sane naming system would keep the words separate.

The current system heard that suggestion, opened the thesaurus, and blacked out.

OpenAI put Pro on the model and the bill​

OpenAI's public GPT-5.6 naming is not hard by itself. In the GPT-5.6 Sol preview, OpenAI says the number identifies the generation while Sol, Terra, and Luna identify capability tiers. The pricing also makes the ladder obvious. Sol is $5 per million input tokens and $30 per million output tokens. Terra is $2.50 and $15. Luna is $1 and $6.

That part is fine. Sun, Earth, Moon. You can quibble with the astronomy department later.

The trouble starts when those names enter the rest of OpenAI's product language. GPT-5.5 is available in ChatGPT and Codex, and gpt-5.5-pro is a separate API model priced at $30 per million input tokens and $180 per million output tokens. At the same time, ChatGPT Pro is a subscription plan, except there are two Pro tiers. Pro $100 gives 5x higher usage than Plus. Pro $200 gives 20x higher usage than Plus.

So "Pro" can be a model suffix, a subscription tier, or a family of subscription tiers. It is doing the work of three nouns while dressed as one adjective.

Codex adds another layer. In the Codex app announcement, OpenAI describes Codex as a desktop app, a command center for agents, and something available across CLI, web, IDE extension, and app surfaces. When someone says a model "will be in Codex," Codex is the product surface. It is where the model runs, not the capability tier.

Then there is effort. OpenAI's API docs say reasoning.effort guides how much the model thinks, with values that can include none, minimal, low, medium, high, and xhigh. That is another axis. It changes runtime behavior for a request. It should not need a brand name, a pricing page, and a support thread.

The safe OpenAI translation is:

OpenAI decoder<br>The same sentence can cross three naming lanes

This is why Pro cannot be read without first asking which axis it is on.

GPT-5.6 Sol<br>model<br>The high-capability GPT-5.6 tier.

GPT-5.5 Pro<br>model<br>A higher-accuracy GPT-5.5 offering.

ChatGPT Pro $100<br>plan<br>A subscription tier with 5x Plus usage.

ChatGPT Pro $200<br>plan<br>A subscription tier with 20x Plus usage.

Codex<br>product<br>A coding agent surface across app, CLI, web, and IDE.

reasoning.effort<br>effort<br>A per-request setting for how much the model thinks.

If a sentence contains "GPT-5.6 Sol in Codex on Pro", you now need a parser, not a product manager.

Anthropic had the clean poem ladder and then added mythology​

Anthropic's original naming ladder did real work. Haiku was small. Sonnet was medium. Opus was large. You could explain it to someone in one sentence and still have most of your lunch break left.

The current Anthropic stack is still more coherent than the others, but it is no longer that simple. The Claude models overview says users should start with Opus 4.8 for complex agentic coding and enterprise work, and use Fable 5 for the highest available capability. The Fable/Mythos launch post says Mythos-class models sit above Opus. Fable 5 is a Mythos-class model with additional safeguards and monitoring. Mythos 5 is the same underlying model with some safeguards lifted for approved customers.

That means the capability ladder now ends at a Mythos-class tier. Fable 5 is the broadly available Mythos-class model. Mythos 5 is the restricted variant with some safeguards...