--- title: "The Right Claude Model for the Right Task" description: "Which Claude model for which task? Haiku, Sonnet 5, Opus 5 and Fable 5 explained, plus how using the right model per task cuts your AI costs." canonical: "https://www.whatsnext-ai.com/blog/right-claude-model-for-the-task" published: "2026-08-20T00:00:00.000Z" updated: "2026-09-09T09:43:16.333Z" --- # The Right Claude Model for the Right Task Which Claude model for which task? Haiku, Sonnet 5, Opus 5 and Fable 5 explained, plus how using the right model per task cuts your AI costs. ![A single claw hammer resting on a rustic wooden workbench](https://fgzcpjbyiakhjifaciaj.supabase.co/storage/v1/object/public/media/right-claude-model-for-the-task.jpg) There is no single best Claude model. The win is matching each task to the cheapest model that does it well: a light model for bulk work, a powerful one only for planning and hard reasoning. That is how you control cost. Most people pick one model, usually the most expensive, and use it for everything. Every message, every file read, every small rewrite, at the top rate. That is the fastest way to burn through a budget, and it is almost never necessary. Looking for "Claude 5"? There is no single release by that name; Anthropic versions each tier on its own schedule. This post explains the current Claude models and what each one is for. More importantly, it shows how to combine them. Not "which model is best", but "which model for which task". New to Claude itself? Start with [what is Claude AI](https://www.whatsnext-ai.com/blog/what-is-claude-ai). ### The models, and what each is for Anthropic does not ship a single "Claude 5". It ships a range of models, each with its own role. Here is the current lineup, with the price per million tokens (input/output): - Claude Haiku 4.5 ($1 / $5): the fastest and cheapest model, the lowest price in the lineup. Ideal for high-volume work: reading through documents, sorting, pulling out simple data. Launched October 2025 and still on 4.5. - Claude Sonnet 5 ($2 / $10): out since 30 June 2026, the best combination of speed and intelligence, and in practice the default for most work. Its introductory price became permanent on 10 August 2026. If you are unsure, start here. - Claude Opus 5 ($5 / $25): launched 24 July 2026, Anthropic's own recommended model for complex agent work, code, and business analysis where the reasoning genuinely matters. It comes close to the top model at half the price. - Claude Fable 5 ($10 / $50): the most capable widely available model, out since 9 June 2026, built for long-running agents and the heaviest tasks. Not the fastest, but the strongest, and the most expensive. Anthropic positions Opus 5 for exactly this kind of business analysis: on Box's enterprise document-work benchmark it reports Opus 5 scoring 8% higher than Opus 4.8 overall, with an [11% gain on data analysis and 17% on due-diligence review](https://www.anthropic.com/news/claude-opus-5). There is also a Claude Mythos 5, with the same specs as Fable 5, but invitation-only for defensive cybersecurity. For an ordinary business it is not a choice, so skip it. This lineup and pricing are current as of August 2026. Because these change fast, check [Anthropic's own model overview](https://docs.anthropic.com/en/docs/about-claude/models/overview) for the latest. Think of it as a toolbox. You do not reach for a sledgehammer to drive a screw. Haiku does the bulk work, Sonnet the everyday work, Opus the heavy thinking, Fable the exceptions where you need the absolute ceiling. ### The expensive habit: one model for everything Yet almost everyone reaches for that sledgehammer for everything. They set their most powerful model at the top and let it do every action: read a list of files, search a log, rename a file, write a short summary. The problem: those tasks do not need frontier intelligence. A large share of the work in any AI workflow is reading and searching, and a cheap model handles that fine. By running everything through your most expensive model, you pay top rates for work that should cost a fraction. The bill adds up fast. A team that runs its heaviest model on every task all day burns its monthly budget in days. Not because AI is expensive, but because the allocation is wrong. ### Think in roles, not turns The fix is not a cheaper model. It is a better structure. Instead of thinking "which model do I use in this conversation", you split the work into roles and give each role a model: - The executor does the actual work: writing, building, editing. Here you put a strong model (Opus 5), or Sonnet 5 if cost matters more than the last few percent of quality. - The planner and verifier makes a plan before anything gets built, and checks the result before it ships. This role only reads and reasons, changes nothing, and fires rarely. That is exactly where you put the most capable model (Fable 5), because a mistake in the plan is the most expensive mistake there is. - The worker does the heavy reading and searching: digging through logs, pulling data, summarising documents. It hands back a short summary instead of pushing everything through the expensive model again. This is a job for a cheap model (Sonnet 5 or Haiku 4.5). The effect: your most expensive model only fires when it is worth it, on planning and verifying. The rest of the work runs on models that cost a fraction. Same output, much lower bill. The biggest saving is not even switching models, it is two disciplines. Plan before you build, because without a plan work drifts the wrong way, and correcting that drift is the largest token sink there is. And push heavy reading out to cheap workers, so your main process stays lean. ### Why this matters for your business For an individual, this is a handy trick. For a business automating a process, it is a design decision about cost. When we build an AI system for a client, "which model per step" is an architecture choice, not an afterthought. Every step in a process gets the cheapest model that can handle it reliably. Extracting a document does not need the same model as the final decision. In practice that saves a multiple in cost, without lowering quality where it counts. And because the models change fast (four releases in ten weeks, Opus 4.8 went from flagship to older version in that window, and OpenAI's [GPT-6 Astra](https://www.whatsnext-ai.com/blog/gpt-6-astra) shows the same pattern outside the Claude family), we build model-agnostic. Moving to a better or cheaper model is then a setting, not a rebuild. That is exactly why we build in code and not on a low-code layer that locks you to one provider. More on that in [model-agnostic AI architecture](https://www.whatsnext-ai.com/blog/model-agnostic-ai-architecture) and [custom AI development](https://www.whatsnext-ai.com/custom-ai-development). ### Do it yourself: Think in Roles, Not Turns Do you work with Claude Code yourself and watch your usage climb? The idea is to split your work into three roles and pin a model to each: an Executor (Opus 5) for the actual building, a Planner and Verifier (Fable 5) that plans before you build and checks before you ship, and a Worker (Sonnet 5 or Haiku 4.5) for heavy reading and search. Your most expensive model then only fires for planning and verifying, not on every turn. We put the exact setup into a short, practical guide: the three roles, the model to attach to each, the five-minute configuration, and the two disciplines that save the most. [Download the guide: Think in Roles, Not Turns](https://www.whatsnext-ai.com/downloads/think-in-roles-not-turns.md) ### What this means for you The choice between Haiku, Sonnet, Opus and Fable is a real one, but it is a detail inside a bigger question: is your AI setup built so each task gets the right model, and so you can switch the moment something better arrives? Want to make that concrete for your own processes? [Book a free strategy call](https://www.whatsnext-ai.com/contact) and we will look together at which model fits which step, and how to keep your AI costs under control. *Sources: model names, pricing and release dates come from Anthropic's own [model overview](https://docs.anthropic.com/en/docs/about-claude/models/overview) and product announcements; the Opus 5 vs Opus 4.8 benchmark figures are from [Anthropic's Claude Opus 5 announcement](https://www.anthropic.com/news/claude-opus-5), citing Box's enterprise document-work evaluation. Pricing is in US dollars and current as of August 2026.*