Breaking Down API Token Pricing: Caud Opu 4.8 vs GPT-5.6 Models

Why Token Pricing Matters More Than You Think

If you’re building with large language models, token pricing is the lever that controls your bottom line. A single prompt with 10,000 input tokens and 2,000 output tokens can cost anywhere from a few cents to nearly a dollar depending on the model you choose. For teams running thousands of API calls daily, those pennies add up fast.

In this article, we’ll break down the exact input and output token costs for two of the most capable model families available today: Caud Opu 4.8 (Anthropic) and the GPT-5.6 series (OpenAI). We’ll also show you how OneMux wraps all these models behind one API, giving you the flexibility to route to the cheapest or most capable model without changing your code.

Understanding Input vs. Output Token Costs

API pricing typically splits token costs into two buckets

• Input tokens: Everything you send to the model — system prompt, user messages, context, and any attached content like images or documents.
• Output tokens: The model’s response. Because generating tokens is computationally heavier, output costs are almost always higher.

The Asymmetry of Pricing

Most providers charge 3x–6x more for output tokens than input tokens. This asymmetry means that models with high output token consumption — like those used for long-form generation, chain-of-thought reasoning, or agentic workflows — can become expensive quickly.

For example, if you’re using a model to write a detailed report with 10,000 output tokens, the cost is dominated by the output rate. Understanding the per‑million‑token prices helps you estimate bills before you run.

Caud Opu 4.8 Pricing Breakdown

Available through OneMux, Caud Opu 4.8 by Anthropic is designed for complex reasoning and agentic tasks. Its token pricing is among the most competitive for its performance tier:

• Input: $2.50 per 1 million tokens
• Output: $12.50 per 1 million tokens
• Cached input: Not separately advertised by Anthropic, but OneMux will pass through any per‑model caching benefits as they become available.

At these rates, a conversation with 50,000 input tokens and 5,000 output tokens costs:

Input:  50,000 / 1,000,000 * $2.50 = $0.125
Output: 5,000 / 1,000,000 * $12.50 = $0.0625
Total:  $0.1875

That’s less than 20 cents for a substantial interaction — far cheaper than older models with similar capabilities.

GPT-5.6 Sol, Terra, Luna Pricing

OpenAI’s GPT-5.6 family includes three variants: Sol, Terra, and Luna. According to OpenAI’s official pricing page, direct API pricing for GPT‑5.6 Sol is:

• Input: $5.00 / 1M tokens
• Cached input: $0.50 / 1M tokens (90% discount)
• Output: $30.00 / 1M tokens

However, OneMux offers these models at significantly lower rates through its optimized routing infrastructure. Here are the OneMux prices for all three variants:

Note: OneMux pricing for GPT‑5.6 Sol is already 60% lower on input and 50% lower on output than direct OpenAI rates. The cached input discount from OpenAI ($0.50/M) is not reflected in the OneMux table, but OneMux passes through any per‑model cache savings when available.

Cached Input Tokens: A Hidden Cost Saver

Many developers overlook the power of cached input tokens. When you send the same system prompt or context repeatedly (e.g., in multi-turn conversations or batch requests), providers can reuse previously processed tokens at a fraction of the cost.

On GPT‑5.6 Sol directly from OpenAI, cached input runs $0.50 per 1M tokens — a 90% discount from the full input price. If you have a 100,000‑token system prompt that you reuse across hundreds of user queries, caching can save you hundreds of dollars per month.

On OneMux, caching benefits depend on the underlying model provider. For models that support it (like GPT‑5.6 Sol direct), the savings are automatically applied. Always enable caching in your API calls if your use case involves repeated context.

Comparing Costs Across Models

To help you decide which model fits your budget, here’s a side‑by‑side comparison of the two families using OneMux pricing:

Key takeaway: Caud Opu 4.8 offers the lowest output cost among these premium models, making it ideal for tasks that generate long responses. GPT‑5.6 Sol has a slightly cheaper input cost but more expensive output.

How OneMux Simplifies Multi‑Model Token Management

Juggling multiple API keys, pricing tiers, and authentication schemes is a pain. OneMux solves this by providing a single OpenAI‑compatible endpoint for all the models above. With one API key, you can:

• Route requests to any model without changing your code.
• View spend per model and per API key in real time.
• Top up credits with a pay‑as‑you‑go model — no monthly commitments.
• Access lower prices than going direct to each provider.

OneMux doesn’t invent uptime numbers or discount promises beyond what’s published on pricing. What we do guarantee is a transparent, unified interface and the freedom to switch models without rewriting your app.

For more details on setting up your first route, see the Quickstart guide.

Practical Tips for Reducing Token Spend

1. Use shorter system prompts — every token counts. Trim unnecessary instructions.
2. Leverage caching — if your use case repeats context, explicitly enable cached tokens.
3. Choose output‑cheap models — for long generations, Caud Opu 4.8’s $12.50/M output is hard to beat.
4. Batch similar requests — combine multiple inputs into one API call to reuse context.
5. Monitor usage — OneMux’s dashboard shows token consumption per model; set alerts for budget thresholds.

Frequently Asked Questions

What’s the difference between input and output token pricing?

Input tokens are the text you send to the model (prompts, context). Output tokens are the generated response. Output tokens cost more because generating them requires more computation.

How do cached input tokens work?

Cached input tokens are reused across requests with identical context. Providers can skip re‑processing them, so you pay a fraction of the full input price. On GPT‑5.6 Sol direct, cached input is $0.50/M vs. $5.00/M.

Which is cheaper: Caud Opu 4.8 or GPT‑5.6 Sol?

Caud Opu 4.8 has lower output cost ($12.50 vs $15.00 per 1M tokens) but slightly higher input cost ($2.50 vs $2.00). For output‑heavy tasks, Opu wins; for input‑heavy tasks, Sol is cheaper.

Does OneMux support caching?

OneMux passes through per‑model caching benefits when the provider supports them. Check the model’s documentation details on OneMux Docs.

How do I start using these models with OneMux?

Sign up at https://onemux.net, top up credits, and generate an API key. Then call the unified endpoint with the model field set to your chosen model. Full examples in the Quickstart.

Conclusion

Token pricing doesn’t have to be a guessing game. By understanding input and output rates — and taking advantage of caching — you can run advanced AI workloads without blowing your budget. Caud Opu 4.8 offers excellent value for reasoning and generation, while the GPT‑5.6 series provides powerful alternatives with competitive input pricing.

OneMux brings all these models together under one API, with transparent pricing and no lock‑in. Explore the full model catalog at OneMux Models and start optimizing your token spend today.

Sources

• OpenAI API Pricing — https://openai.com/api/pricing/ (accessed May 2025): reports GPT‑5.6 Sol direct pricing at $5.00/1M input, $0.50/1M cached input, $30.00/1M output.
• OneMux Model Catalogue — https://onemux.net/models (accessed May 2025): lists current routed pricing for Caud Opu 4.8, GPT‑5.6 variants, and more.