GPT-5.6 or Opus 5, which model for which task

The question "GPT or Claude" is usually asked wrong. People ask "which one is better", as if there were a table with a winner. The right question is "which one fits my task and my budget". And the answer is often "a third, cheaper model". This playbook walks you through ten steps, at the end of which you make a reasoned choice instead of a gut call. It is for you if you are starting to build seriously with AI and no longer want to grab whatever is trending.

The specific names in this playbook are the state as of 31 July 2026. The decision logic stays when the next generation arrives, you just swap the names.

Step 1, the tier question first, the vendor question second

Before you put GPT against Claude, settle the tier. Both providers have three tiers, but they split them differently. Claude goes by size: Haiku 4.5 as the budget tier, Sonnet 5 as the middle class and Opus 5 at the top. OpenAI has gone by role since the 5.6 generation: Luna for fast and cheap, Terra for balanced everyday work, Sol for the hardest cases. Most waste does not happen between providers, it happens within one. People take Opus for a task Haiku would have handled and pay five times the price. So ask first: do I even need the top tier, or is the middle one enough.

Step 2, when Opus 5 is the right choice

Opus 5 plays to its strength on two things. Deep, multistep reasoning where the model has to hold a plan and stay consistent across many steps. And agent work where the model calls tools and decides on its own. If you are building a coding agent that works on a codebase for hours, or an analysis that needs twenty intermediate steps, Opus 5 is worth its money. On short, clear tasks you will barely notice the difference, but you still pay for it.

One detail that belongs in the cost calculation since 24 July 2026: Opus 5 thinks by default. That is the reason for its strength, but the thinking tokens count against your max_tokens limit and land on your bill. How to control that is in the Opus 5 migration playbook.

Step 3, when GPT-5.6 is the right choice

GPT-5.6 is the obvious choice if you are already inside the OpenAI ecosystem, so Assistants, the OpenAI tools or existing integrations. It also belongs in your stack if you deliberately want vendor diversity so you do not depend on one provider. Sol is the tier OpenAI itself puts forward for coding and agentic work. And there are classes of task where teams see GPT ahead in their own tests. Which ones those are for you, you only find out through step 8, not through other people's benchmarks.

Step 4, the context factor

The picture shifted here in July 2026. The large context window used to be a separate Claude variant you had to request explicitly, written as claude-opus-4-8[1m]. On Opus 5, one million tokens is both the default and the maximum, there is no smaller variant anymore, so you get the large context without doing anything. Sonnet 5 and Gemini 3.6 Flash also sit at one million.

That makes context size a weaker differentiator between top models than it once was. If your task means processing an entire repository, a long contract or a complete conversation history in one go, check the current context size on the official model pages, because these numbers change fast. The more interesting question these days is no longer whether a million fits, but whether the model keeps working cleanly across the full length.

Step 5, the cost comparison, honestly calculated

Opus 5 costs 5 dollars per million input tokens and 25 dollars per million output tokens, exactly what its predecessor Opus 4.8 cost. Sonnet 5 sits at an introductory 2 and 10 dollars until 31 August 2026 and at 3 and 15 after that. The exact GPT-5.6 prices are on the OpenAI model page and you should look them up live rather than take them from a blog post, especially since OpenAI cut them sharply on 30 July 2026, Luna by 80 percent and Terra by 20.

The important point is not the price per model, it is the bill for your volume. A thousand requests a day at 2000 output tokens each add up. Moving one tier down, from top to middle, often halves or thirds the bill without the quality dropping noticeably for your task.

Step 6, tool use and agent capability

If you are not just generating text but building an agent that calls tools, what counts is not language quality but how reliably the model picks the right tools in the right order and knows when to stop. Both top models are strong here, but they behave differently. Some models would rather call one tool too many, others one too few. For an agent that is often more decisive than any reasoning benchmark. Test it with your concrete tool set, not in the abstract.

Step 7, language and German-market quality

If you need German-language output that does not read like translated English, that is a dimension of its own. Both models can do German, but the tone, the idiom and the handling of technical terms differ. You only notice that if you give both the same task and put the answers side by side. An English benchmark tells you nothing about it. For a German-speaking audience that is a real factor, not a detail.

Step 8, the test instead of the opinion

This is the most important step. Collect ten to twenty real tasks from your own work, not invented ones. Send exactly the same prompts to both models and put the answers side by side. Judge them by your criteria, so correctness, tone, format, speed. After twenty real cases you know more than after a hundred foreign benchmarks. There is a playbook just for that, Agent eval in 60 minutes, which shows you how to build such a mini eval.

Step 9, mixed operation is usually the best answer

You do not have to decide on one model at all. The most productive setup is often a router. A cheap, fast model catches the easy cases, and only the hard ones get passed through to the expensive top model. That way you pay the top price only for the twenty percent of requests that really need it. Exactly this pattern, a tiered model deployment, saves the most money in practice at unchanged quality. How to set that up concretely in your daily driver is in the playbook Cost controls for daily drivers.

Step 10, the decision in one sentence

If you need deep reasoning or long contexts and have the budget, take Opus 5. If you are inside the OpenAI stack or deliberately want vendor diversity, take GPT-5.6. If your task is simple, take the middle or budget tier in either case and save the money. And whatever you suspect, verify it with your own mini eval from step 8 before you commit.

What next

If you want to understand the model landscape in principle before you compare, start with the model comparison lesson in level 1. If you are already on Opus and want to move cleanly to the current generation, the Opus 5 migration playbook takes over. And if you want to build the mixed operation from step 9 seriously, the eval playbook is your next stop.

Source

Current model data, context sizes and prices change fast. Look them up at the source before any final decision, not in blog posts.

• Claude model overview (Opus 5, Sonnet 5, Haiku 4.5, pricing, context): https://platform.claude.com/docs/en/about-claude/models/overview
• OpenAI model overview (GPT-5.6 Sol, Terra, Luna, pricing, context): https://platform.openai.com/docs/models
• Claude Opus 5 announcement: https://www.anthropic.com/news/claude-opus-5