Claude Fable 5 vs GPT-5.5 API: Pricing, Token Costs, and OneMux Routing

Two numbers are dominating AI infrastructure conversations: $10 and $50. That is the per-million-token input and output price quoted for Claude Fable 5 in a recent Finout benchmark. Mythos 5 sits nearby in the same comparison, and developers are trying to decide whether the GPT-5.5 API is still the cheaper default.

This article is a practical guide to Claude Fable 5 API costs, how they compare with GPT-5.5-class models, and why the most cost-efficient teams route prompts to the right model instead of paying a premium everywhere.

What the Finout benchmark says about Claude Fable 5 pricing

According to Finout's Claude Fable 5 and Mythos 5 pricing benchmark, Claude Fable 5 is priced at $10 per million input tokens and $50 per million output tokens. The article frames that as roughly 2x the GPT API price shown in the same benchmark.

It is important to remember that this is a benchmark quote, not a universal price. OneMux currently lists Claude Fable 5 at $5 per million input tokens and $5 per million output tokens, so provider-specific rates can change the economics significantly.

Claude Fable 5 vs GPT-5.5-class API pricing

The phrase GPT-5.5 API is still what many teams search for when they mean current-generation OpenAI chat completion pricing. In OneMux's catalogue, the GPT-family models are GPT-5.6 Terra, GPT-5.6 Luna, and GPT-5.6 Sol. The table below puts their per-token rates next to Claude Fable 5.

Rates change and plans differ, so use this as a reference, not a guarantee. Always check the OneMux pricing page for the latest numbers.

Why output tokens are the real cost driver

A low input price can hide an expensive output side. Suppose you send 120,000 input tokens and generate 8,000 output tokens. At the Finout benchmark rates, the cost is:

input_tokens = 120_000
output_tokens = 8_000

# Finout benchmark rates
finout_cost = input_tokens * 10/1_000_000 + output_tokens * 50/1_000_000

# OneMux-listed Claude Fable 5 rates
onemux_cost = input_tokens * 5/1_000_000 + output_tokens * 5/1_000_000

print(finout_cost, onemux_cost)  # 1.60 0.64

That 60% difference is exactly why you need to look at your real token mix before choosing a model.

Context length, caching, and batch pricing

Tokens are not the only variables. Context windows determine how many input tokens fit in one request, prompt caching can reduce repeated input costs, and batch APIs often cut prices in exchange for latency. None of those details appear in the headline per-token price, yet all of them change the final invoice.

The lesson for teams evaluating Claude Fable 5, Mythos 5, or any GPT-5.5-class model is simple: read the fine print for cache pricing and minimum batch sizes before you assume the list price is your effective price.

Where does Mythos 5 fit in?

Finout groups Mythos 5 with Claude Fable 5, but public Mythos 5 pricing is not fleshed out in the source material. If you are evaluating Mythos 5, use the same framework: input price × input tokens + output price × output tokens. Without transparent per-token rates, you cannot compare it with GPT-5.5 API models in a meaningful way.

Use OneMux to route around the most expensive model

The strategic shift is from one-model contractors to a portfolio of models. OneMux gives you a single OpenAI-compatible API for access, routing, keys, spend visibility, credit top-ups, and pay-as-you-go usage. You can keep Claude Fable 5 for tasks where its output pattern is worth the premium, and switch to GPT-5.6 Luna for high-volume, lower-stakes requests.

Start by reviewing the OneMux models page to see current listing prices, then set up your first key through the OneMux quickstart guide. For details on request handling and streaming, the API documentation is the reference.

The goal is not to avoid premium models forever. It is to make sure every prompt earns its cost.

Make pricing a product decision

Claude Fable 5's $10/$50 benchmark price is a useful reference point, not a universal fact. The real cost depends on your token mix, caching, batch usage, and provider. When you separate the quoted rate from the actual cost per task, you can make smarter calls—and OneMux makes that separation practical with transparent listing and unified routing.

Check the source benchmark, run your own token math, and keep Mythos 5 on your radar. The best AI budget is the one you can explain in a single Excel formula.

Sources

• Finout — Claude Fable 5 and Mythos 5: Pricing Benchmarks