Which Claude Model Should You Use? Opus, Sonnet and Haiku by Job in 2026

Anthropic cut Claude Opus 5.5's price by 20% and shipped it on September 22, 2026, minutes before OpenAI answered with two cheaper GPT-6 models of its own, Sol and Luna, sitting alongside the earlier GPT-6 Astra. That timing matters less than the math underneath it.

Claude now ships in four tiers: Fable 5.1, Opus 5.5, Sonnet 5 and Haiku 4.5. Each one is priced roughly two and a half times apart from its neighbor, and picking the wrong tier for a given job either wastes money or wastes your own time waiting on a model that was never fast enough for the task. This guide sorts the four by the job, not by the launch date, using Anthropic's pricing docs and model comparison table as of today.

How to Choose Between Claude's Four Tiers

Anthropic's own guidance boils the decision down to three questions, before you look at any single benchmark: how capable does the output need to be, how fast does it need to arrive, and what is the budget for the volume involved. A fourth lever most people skip is the effort parameter, which trades intelligence for latency and cost inside a single model rather than forcing a switch to a different one.

The practical version: start efficiency-first with Haiku 4.5 if the task is high-volume or latency-sensitive, and upgrade only when you hit a specific capability gap. Or start capability-first with Sonnet 5 or Opus 5.5, and step down once evals show a cheaper tier holds up.

Either path works. What does not work is picking a tier once, by reputation, and never testing whether a cheaper option is good enough.

Combining Models Instead of Picking Just One

Anthropic's own documentation describes a third option that skips the single-model decision entirely: pairing a cheap model with a frontier one so most tokens bill at the lower rate. Two patterns show up most often in production systems built this way.

An executor-advisor pattern uses a fast, cheap model to handle routine steps and escalates only the hard decisions to a frontier model. An orchestrator pattern has a capable model delegate bulk work to a swarm of cheaper workers, then reviews the results. Both cut cost without giving up quality on the parts of the job that need it.

This is worth considering before defaulting to a single tier for an entire application. A support bot, for instance, might run Haiku for the first response and escalate to Sonnet only when a customer's question does not match a known pattern. A coding agent could run the same way: Haiku classifies which files a change touches, Sonnet writes the patch, and Opus 5.5 only gets called in when a test suite fails and the agent needs to reason about why.

Neither pattern requires locking the whole application to one price point. It requires deciding, task by task, which tier that specific step actually needs.

The Shortlist: All Four Tiers at a Glance

Haiku's context window is the outlier here, a fifth the size of the other three at 200,000 tokens. Its reliable knowledge cutoff is also 11 months older than Sonnet's, which matters for anything that changed since early 2025.

The quick version, ahead of the job-by-job detail below: default to Sonnet unless you have a specific reason not to.

• Writing, coding, research, most chat work: Sonnet 5
• Multi-hour autonomous coding, large refactors, computer-use tasks: Opus 5.5
• Everything you'd give Opus, but it still isn't enough on your own evals: Fable 5.1
• High-volume, latency-sensitive, or genuinely simple tasks: Haiku 4.5

Which Model for Coding

Anthropic's model-selection matrix puts complex agentic coding and enterprise work under Opus 5.5, with example use cases like multihour autonomous agents and large-scale refactoring. Everyday coding and agentic tool use sits under Sonnet.

The benchmark gap is narrower than the price gap suggests. Sonnet scores 82.1% on SWE-bench Verified, the first Claude model to clear 80% on that benchmark. Opus 5.5 posts 89.9% on the harder SWE-bench Pro suite, a genuinely different benchmark, not just a bigger number on the same one.

For a solo developer or small team working through an editor or CLI, the honest default is Sonnet: fast enough for a real-time loop, and $2/$10 per million tokens keeps a busy day of tool calls affordable. Reach for Opus 5.5 specifically when a task runs unattended for hours, where a wrong turn three steps in is expensive to unwind. Windsurf is one example of an editor that lets you swap the underlying model without rewriting your prompts, and HokAI's directory of coding assistants lists more.

Effort tuning matters here as much as the model choice. Sonnet defaults to high effort out of the box, while Opus 5.5 defaults to the lighter medium, which is part of why its per-token price looks aggressive next to Opus 4.8: a chunk of the saving comes from a different default setting, not only the base rate. Raising Opus 5.5 to xhigh for a genuinely hard problem, then dropping back down once it is solved, is usually cheaper than staying at the top setting for an entire session.

Which Model for Research and Deep Work

Research splits into two different jobs. Quick lookups and synthesizing a handful of sources are Sonnet territory: it posts 96.2% on GPQA Diamond and 84.7% on ARC-AGI-2, both strong enough that spending Opus-tier money on a straightforward question is money left unspent for no gain.

Multistep deep research is different. That is the job Anthropic's own matrix names for Fable 5.1: research that chains searches, reads full documents and reconciles contradictions across sources for twenty minutes or more, carried through to a finished document.

If the job is closer to fact-checking a claim against academic literature, a dedicated tool like Consensus pairs well with either model, finding sources while Claude does the synthesis. HokAI's deep-research roundup and directory of deep-research tools cover what a research-specific tool can see that a general chat model cannot, and a separate breakdown looks at the same question from the researcher's side rather than the model's.

Which Model for Writing and Editing

For drafting, editing and day-to-day writing, Sonnet is the tier Anthropic itself points to: strong reasoning without flagship pricing. The million-token context window means a full manuscript or a stack of prior drafts fits in one prompt, without the summarization tricks smaller-context models used to require.

Haiku is worth considering only for high-volume, formulaic writing: subject lines, short product blurbs, or a rough first pass you plan to rewrite. Its $1/$5 price and fastest-in-lineup latency are built for that kind of throughput, not for prose meant to publish unedited. If a writing tool such as Jasper or Writesonic lets you pick a model, the same logic applies regardless of which app is making the call, and HokAI's directory of writing assistants covers the options beyond those two.

Which Model for a Resume or Job Application

A resume is short, so the price gap between tiers amounts to a few cents either way. The decision should be made on quality, not cost, and that favors Sonnet over Haiku even for a document this size.

Sonnet's stronger reasoning shows up in the part of a resume rewrite that actually matters: reframing a job history around the target role's real requirements, rather than restating a job title with better adjectives. Kickresume and Teal are both built specifically for this; HokAI's resume-building roundup and directory of resume builders compare the full set against real ATS-parsing behavior.

Which Model for an Always-On Agent

The autocomplete data behind this guide surfaced a specific real question: which model to run behind OpenClaw, the self-hosted agent that connects to WhatsApp, Telegram and other chat apps and keeps running in the background on your own machine. OpenClaw is explicitly model-agnostic. Its own site describes bringing hosted, subscription-backed or local models, with Claude listed as one supported provider among several.

For an agent that sits idle most of the day and then has to plan a multi-step task on short notice, Sonnet is the sensible default. It is inexpensive enough to run continuously and fast enough for the tool-calling loop the framework depends on. Escalate individual steps to Opus 5.5 only when a step needs deeper reasoning, rather than routing every background check through the pricier tier.

Enterprise platforms follow the same math at larger scale. Sierra, which builds AI agents for customer service, faces the identical tradeoff between a fast default model and an escalation path for the harder cases. A meeting-notes tool like tl;dv that feeds an agent's task queue is a good example of the steady, low-complexity input Haiku can also handle before Sonnet takes over the planning step. HokAI's agentic AI roundup covers this job-first logic across tools that aren't tied to one model provider.

When Even Opus Isn't Enough: Fable 5.1

Fable 5.1 is not simply a bigger Opus. Anthropic's docs name it specifically for when evals on Opus 5.5 at higher effort still fall short on demanding reasoning or long-horizon agentic work, and it extends the earlier Fable 5 at the same input and output prices, with cache reads at a quarter of the cost.

At $10 input and $50 output per million tokens, it costs 2.5 times what Opus 5.5 does. That is why Anthropic treats it as a last step, not a starting point: implement with Opus, optimize the prompt, evaluate, and only move up if the evaluation still misses the bar.

The premium does not win everywhere. On Terminal-Bench 4.0, an agentic coding benchmark, Opus 5.5 actually scored higher than Fable 5.1 at launch: 66.4% against 55.8%, according to SiliconANGLE's coverage of Anthropic's own release figures. A higher price is not a guarantee of a higher score on every task, which is the whole argument for testing rather than assuming.

What Each Tier Costs, With a Worked Example

Anthropic's pricing docs include a worked example that makes the gap concrete. A one-hour coding session on Opus consuming 50,000 input tokens and 15,000 output tokens comes to $0.705 without prompt caching.

With caching active, that same session drops to $0.525. Run the equivalent math on Haiku for a lighter, high-volume job: Anthropic's own customer-support example lands around $37 for 10,000 tickets at roughly 3,700 tokens each.

Opus 5.5 also introduced a fast mode in research preview, at a premium, for roughly 2.5x higher output speed on the API only. If you are still running Opus 4.8, the price cut alone is reason to re-run your existing evals against 5.5 before renewing any volume commitment.

Who Should Skip Opus and Fable Entirely

Anthropic's own efficiency-first path exists for a reason. For high-volume, straightforward tasks and tight-latency applications, starting with Haiku and upgrading only on a proven capability gap is the recommended approach, not a compromise.

• Classification and tagging at scale
• Simple extraction from structured text
• High-throughput pipelines where speed matters more than nuance
• Cost-sensitive deployments already validated against real evals

The price difference compounds fast at volume. The same batch that costs roughly $37 on Haiku would run several times higher on Sonnet and higher still on Opus, for accuracy gains a straightforward classification task is unlikely to need.

The Case for Starting With Opus Anyway

The strongest argument against everything above is Anthropic's own capability-first alternative: implement with Opus 5.5 from day one, then downgrade once evals prove a cheaper tier holds up. For a small team without time to build a proper evaluation set, that is not an unreasonable shortcut.

Guessing that a cheaper tier is good enough, and shipping it untested, is how quality regressions reach production quietly. Anthropic's own docs are explicit that tuning the effort parameter, not switching models, is usually the better first lever if a workload feels slow or expensive.

Both paths converge on the same requirement: real evals, not intuition, deciding whether to move. Start wherever matches your actual constraint, and let testing do the rest of the work.

Neither approach is free to skip. A team that starts capability-first and never revisits the choice ends up paying Opus prices for Haiku-shaped work a year later. A team that starts efficiency-first and never re-tests as the job grows ends up shipping a support bot that confidently gets the hard 10% of tickets wrong. The failure mode on both sides is the same: treating the first model chosen as a permanent decision instead of a hypothesis to keep checking.

Data Handling: Does Claude Train on What You Send It?

A separate but common question sits underneath all of this. Anthropic's privacy center states that by default, inputs and outputs from commercial products, including the API and Claude for Work, are not used to train its models. That only changes if you explicitly opt in.

Consumer app usage, meaning the free, Pro and Max plans at claude.ai, runs under a separate policy. A work task run through the consumer app is not automatically covered by the same commercial-product default, so the distinction matters if the job involves anything sensitive.

Regulated teams have one more knob worth knowing about. On Claude 4.6 and later models, including every tier in this guide, pinning inference to inference_geo: "us" keeps processing inside the United States for a 1.1x price premium across every pricing category. Global routing, the default, uses standard pricing and does not guarantee where a given request is processed. Neither option changes the training policy above; it only changes where the computation physically happens, which is a separate axis entirely from what gets logged or retained.

What Would Change This Answer

Two things will date this guide fast. Anthropic has signaled that Sonnet 5.5 and Haiku 5.5 are coming within weeks of the Opus 5.5 launch, which would reset the prices and benchmarks above the day they ship. Fable 5.1 also has a sibling most readers will never touch: Mythos 5.1, the same capability set restricted to participants in Anthropic's Project Glasswing program, priced identically but not generally available.

Separately, Haiku 4.5's retirement commitment on Anthropic-operated platforms is October 15, 2026, just weeks past this guide's publish date. That is a floor, not a forecast, but anyone building production infrastructure on Haiku 4.5 today should plan for a migration rather than assume the model ID stays pinned indefinitely.

If none of the four tiers feels like a confident fit, comparing against every model on the market is worth the extra ten minutes. HokAI's model recommender and full model leaderboard rank every GA model by price, benchmark score and speed side by side.

For a broader look across every frontier lab, not just Anthropic, HokAI's guide to choosing the right LLM lines Fable 5.1 up against GPT-6 Astra, Gemini and DeepSeek's current releases. Smart Match will ask about your actual constraints and suggest a shortlist instead of a single guess.

The pattern underneath all four tiers holds even as the names change: pay for reasoning depth only on the fraction of a workload that actually needs it, and let the job, not the launch date, decide which model to open.

Frequently asked questions

Which Claude model should I use for coding?

Sonnet 5 for everyday coding and agentic tool use; it scores 82.1% on SWE-bench Verified at $2/$10 per million tokens. Reach for Opus 5.5 (89.9% on SWE-bench Pro) for multi-hour autonomous refactors or systems work where an unattended run needs more reliability than speed.

Is Claude Opus 5.5 worth the extra cost over Sonnet 5?

Only for jobs Anthropic itself scopes to Opus: long-running agentic coding, computer-use automation, and enterprise workloads where reliability over hours matters more than the roughly 2x price gap. For everyday chat, writing and coding, Sonnet 5 matches Opus-tier usefulness at less than half the price.

What is Claude Fable 5.1 and when should I use it instead of Opus?

Fable 5.1 is Anthropic's tier above Opus 5.5, priced at $10/$50 per million tokens, built for demanding reasoning and long-horizon agentic work. Anthropic's own guidance says to move to it only after evaluations on Opus 5.5 at higher effort still fall short, not as a default starting point.

Is Claude Haiku 4.5 good enough for production use?

Yes for high-volume, latency-sensitive or straightforward tasks like classification, tagging and simple extraction, where its $1/$5 pricing and fastest-in-lineup latency pay off. Its smaller 200K-token context window and February 2025 knowledge cutoff make it a poor fit for long-document work or anything needing current information.

Which Claude model should I use with OpenClaw or a similar AI agent?

Sonnet 5 as the default planning model, since OpenClaw and similar self-hosted agent runtimes are model-agnostic and run continuously: Sonnet 5's price and speed suit an always-on tool-calling loop. Escalate specific sub-tasks to Opus 5.5 only when a step needs deeper reasoning, rather than routing every background check through the more expensive tier.

Covered in this guide

• Opus 5.5: Anthropic's September 2026 flagship LLM with a 1 million token context window, built for long-running agentic coding and computer use.
• Sonnet 5: Claude Sonnet 5, released by Anthropic on June 30, 2026, hits 82.1% SWE-bench Verified with a 1M-token context window.
• Fable 5.1: Anthropic's second Mythos-class model, released Sept 1, 2026, leading rival frontier models with a 1 million token context window.
• Opus 4.8: Claude Opus 4.8 (May 2026): Anthropic's most capable model. 1M context window, 88.6% SWE-bench Verified, $5/$25 per 1M tokens. Excels at long-horizon coding and complex reasoning.
• Consensus: AI-powered search engine for peer-reviewed research literature
• Jasper: The agent workspace built for modern marketing teams to orchestrate intelligent agents and deliver end-to-end marketing workflows.
• Kickresume: AI resume and cover letter builder used by more than 8 million job seekers, pairing AI-assisted writing with ATS-friendly templates and a website builder.
• Sierra: Enterprise AI agent platform from Bret Taylor valued at $15.8B in 2026, used by 40% of the Fortune 50 for customer service across chat, voice, and email.
• Teal: Teal is a free AI job search platform for job seekers, built by Teal Labs, founded in 2019, combining an unlimited resume builder and application tracker.
• tl;dv: AI meeting notetaker turning every meeting into insights across your entire company
• Windsurf: Windsurf is an agentic AI IDE by Cognition AI featuring Cascade agents, Codemaps, and integrated Devin cloud workflows — used by developers in 70+ languages, starting free with Pro at $20/month.
• Writesonic: Track and grow your brand's presence in ChatGPT, Gemini, and Perplexity with Writesonic's GEO platform. No free plan; paid tiers only.

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