Claude Opus 4.8 vs GPT-5.6 Terra: Benchmarks & Cost

Choosing a foundation model for your product isn't just about who wins on an SMS leaderboard. It's about your actual workload, the shape of your calls, and what each request costs at production scale. If you're weighing Claude Opus 4.8 against GPT-5.6 Terra, you're deciding between two very capable general-purpose models — and the price gap is small enough that the decision often falls to subtleties.

We ran a concrete comparison using a repository-review scenario from BenchLM, then priced the same scenario with the rates listed in OneMux's model catalogue. Here's what you need to know.

A repository review that fits in one request

Repository reviews are a great test case because they stress both context length and output quality. A 50K-token input means the model needs to read a substantial codebase, and a 3K-token output means it has to generate actionable notes without truncation.

According to BenchLM's head-to-head comparison, Claude Opus 4.8 handled a 50K fresh input + 3K output request at a cost of $0.325 using the site's listed standard API rates. The notable finding isn't the dollar amount — it's that the entire review fit in one request. No chunking, no map-reduce, no multi-call orchestration.

That single-request capability matters more than a few cents. Every split means more latency, more memory, more chances for an intermediate step to fail, and more code to maintain.

Token pricing: Claude Opus 4.8 vs GPT-5.6 Terra

The rates below are the standard API prices published in the OneMux model catalogue for current versions of both models. Claude Opus 4.8 is priced at $1.5 per 1M input tokens and $7.5 per 1M output tokens. GPT-5.6 Terra is also $1.5 per 1M input tokens, but its output rate is $9 per 1M tokens.

Let's do the math for the 50K/3K scenario. For Claude Opus 4.8: 50,000 / 1,000,000 × $1.50 = $0.075 for input; 3,000 / 1,000,000 × $7.50 = $0.0225 for output; total = $0.0975. For GPT-5.6 Terra, the input is the same $0.075, but the output is 3,000 × $9 / 1,000,000 = $0.027, bringing the total to $0.102.

So Terra is about 4.6% more expensive on this exact request. But look at the output line: Terra's output is 20% more expensive per token. For workloads that generate long completions — agent logs, memo generation, code synthesis — that difference compounds quickly.

Why the benchmark's $0.325 is different

You might be wondering why BenchLM's figure for Claude Opus 4.8 is $0.325 when our calculation shows $0.0975. The source uses its own listed standard API rates, which may differ from the rates in OneMux's catalogue. We're not going to guess at the exact difference. What we can say is that the comparative shape — Claude Opus 4.8 fitting the entire repository review in one request — is the result worth focusing on. Your actual bill is determined by the provider you call and the rate card they publish. OneMux's catalogue shows current standard rates, and we've presented those numbers exactly as listed.

Practical guidance for choosing

• If your workload is output-heavy, Claude Opus 4.8 is the economical pick. The $1.50-per-1M input price matches Terra, but the output price is 20% lower, so long generations save money every time.
• If you're already invested in the OpenAI ecosystem, GPT-5.6 Terra is a natural fit. The identical input price means most existing cost models still work, and the output premium may be worth the familiarity.
• If you're building an agent that reads large contexts and returns concise results, the two models are nearly tied on price. Let the benchmark behavior decide.
• If you're not ready to commit, route between both models behind OneMux and compare on your own data. The switching cost is a single field in the API request.

OneMux gives you both models behind one API

OneMux is an OpenAI-compatible gateway that provides access to leading models through a single API. You can call Claude Opus 4.8 and GPT-5.6 Terra with the same client SDK you already use, and change models by editing one field in the request body.

Here's a minimal example using the OneMux API with a repository-review prompt:

curl https://api.onemux.net/v1/chat/completions \
  -H "Authorization: Bearer $ONEMUX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4.8",
    "messages": [
      {"role": "user", "content": "Review this repository for common performance issues."}
    ],
    "max_tokens": 3000
  }'

Switch claude-opus-4.8 to gpt-5.6-terra and the same call works against OpenAI's model. No SDK change, no new endpoint, no extra integration work.

OneMux also gives you visibility into spend, lets you top up credits, and provides routing to get reliable model access. If you want to test both models on your own repositories, the quickstart guide shows you how to get an API key and make your first request in minutes. The full documentation covers routing, keys, and spend monitoring.

FAQ

What is Claude Opus 4.8 API pricing?

Claude Opus 4.8 costs $1.5 per 1M input tokens and $7.5 per 1M output tokens through OneMux's catalogue.

How does GPT-5.6 Terra API pricing compare?

GPT-5.6 Terra matches Claude Opus 4.8's input price ($1.5/1M) but charges $9/1M for output, a 20% premium on output tokens.

Can I use both models with the same API?

Yes. OneMux provides an OpenAI-compatible API, so you can switch between model names in the request body without changing your integration.

What is the actual cost for a repository review?

For a 50K input + 3K output request, Claude Opus 4.8 costs $0.0975 and GPT-5.6 Terra costs $0.102 at OneMux's listed rates. BenchLM's own benchmark priced the same scenario at $0.325 using its listed rates.

Conclusion

Claude Opus 4.8 and GPT-5.6 Terra are close on paper — almost identical input prices, a modest output price gap, and both capable of handling a serious repository review in a single call. But the choice becomes clearer when you look at your output volume and your need for flexibility.

If you're generating a lot of tokens, Claude Opus 4.8 is the economical pick. If you're already on OpenAI's chain, GPT-5.6 Terra is an easy drop-in. And if you don't want to commit to either, OneMux makes it possible to run both through one API, compare the results, and then scale what works. Check the model catalogue to see all available options.

Sources

• BenchLM: Claude Opus 4.8 vs GPT-5.6 Terra