What's the difference between Claude Opus, Sonnet, and Haiku?

They are three tiers of the same model family. Haiku 4.5 is the fast, cheap tier for high-volume shallow work. Sonnet 4.6 is the balanced default that handles most real coding. Opus 4.8 is the premium tier for the hardest reasoning, long agent runs, and unfamiliar codebases. The gap between them is small on simple tasks and widens as the work gets harder.

Which Claude model should I use for coding?

Use Sonnet 4.6 as your default — it scores 79.6% on SWE-bench Verified and handles the bulk of feature work, bug fixes, and refactors. Drop to Haiku 4.5 for the high-volume shallow tail (completions, classification, docs, test scaffolding) and escalate to Opus 4.8 for multi-file refactors, architecture decisions, and debugging that Sonnet misses twice.

Is Claude Opus worth the extra cost over Sonnet?

Only for the hardest 10-20% of tasks. The benchmark gap between Opus and Sonnet is narrow, but Opus is more reliable across long, multi-step agent runs and on codebases it has never seen. For routine work, Sonnet matches it at a fraction of the cost, so the smart move is to reserve Opus rather than default to it.

Claude Opus vs Sonnet vs Haiku: Which Tier to Use (2026)

If you are searching “claude opus vs sonnet vs haiku,” you want one thing: a rule for which tier to point at which task. Here it is.

Default to Sonnet 4.6. Drop to Haiku 4.5 when the work is shallow and high-volume. Escalate to Opus 4.8 only when a wrong answer costs you more than the extra tokens. The three are not really competitors. They are a ladder, and the cheapest reliable setup uses all three at once.

This is the tier-selection version of the question. If you have already narrowed it to the two cheaper models, the Haiku 4.5 vs Sonnet 4.6 head-to-head goes deeper on that specific matchup.

The three tiers at a glance

	Claude Haiku 4.5	Claude Sonnet 4.6	Claude Opus 4.8
Role	Fast floor	Default workhorse	Premium ceiling
Relative cost	~⅓ of Sonnet	Baseline	~1.7x Sonnet
Context window	200K	1M	1M
SWE-bench Verified	73.3%	79.6%	low 80s
Speed	4-5x faster	Standard	Slowest
Reach for it when	Volume and latency beat peak quality	Most real coding	Hardest reasoning, long agent runs

End to end, Haiku is roughly 5x cheaper than Opus. That single ratio is why “just use the best model for everything” is the wrong instinct: on the work that does not need Opus, you are paying five times over for quality you cannot use.

Think about the difficulty curve, not the leaderboard

The mistake is reading three benchmark numbers and picking the highest. The benchmark spread here is small. The price and reliability spread is not, and it is the one that matters.

A better mental model: plot your tasks on a difficulty curve.

The bottom 60% is shallow and repetitive. Classification, extraction, formatting, routing, commit messages, doc generation, IDE-style completions. Haiku produces results that are indistinguishable from Sonnet here, because these tasks never exercise the reasoning that separates the tiers.
The middle 25% is real engineering. Feature implementation, bug fixes, standard refactors, code review. This is Sonnet’s home turf and where it earns “default.”
The top 15% is where reasoning depth pays for itself. Multi-file refactors, architecture decisions, debugging non-obvious failures, and long autonomous agent runs on a codebase the model has never seen. This is the slice worth Opus.

Your job is to find where those lines fall in your workload, then route accordingly. Get the lines right and you keep almost all of Opus’s quality on the tasks that need it and almost all of Haiku’s savings on the tasks that do not.

Haiku 4.5: the fast floor

Haiku 4.5 runs 4-5x faster than the previous Sonnet generation and costs about a third of Sonnet 4.6 per token. At 73.3% on SWE-bench Verified, it is not a toy. It still lands among the better coding models available at any price.

Its job is volume. When you are firing thousands of agent calls a day, the speed is the feature (nobody wants an inline completion behind a spinner) and the price is what keeps the bill sane. Haiku also makes an excellent router and first-pass triager in an agent system: it classifies an incoming task in a fraction of the time, handles the easy ones outright, and only escalates the genuinely hard work upward.

The one limit worth noting is the 200K context window. For file-at-a-time work that is plenty. For dropping a whole mid-sized repo into context, it is not the tool.

Sonnet 4.6: the default

Sonnet 4.6 is the model most teams should reach for first. It scores 79.6% on SWE-bench Verified, carries the full 1M-token context window, and uses meaningfully fewer tokens than Opus on the same real-world coding tasks. It holds multiple constraints in working memory, follows multi-step instructions, and makes fewer false “I’m done” claims than the cheaper tier.

The reason it is the default is coverage. Sonnet handles the widest band of work at a cost you can run all day, which is exactly what you want from the model doing 70% of your coding. It sits close enough to Opus on raw benchmarks that, for most feature work, paying up for Opus buys you nothing measurable.

Opus 4.8: the ceiling

Opus 4.8 is the newest and strongest tier, and the one to use deliberately rather than by default. On benchmarks it sits a hair above Sonnet, in the low 80s on SWE-bench Verified. The benchmark is not where it separates.

Opus pulls ahead on reliability over long horizons. Drive an agent for many turns across an unfamiliar codebase, ask for a refactor that touches a dozen files, or chase a bug whose cause is three layers removed from the symptom, and Opus’s consistency compounds in a way a single benchmark score does not capture. That is the work worth its premium.

The trap is using it everywhere. At roughly 1.7x Sonnet’s price and slower responses, Opus on routine work is pure waste. Reserve it for the problems Sonnet gets wrong twice in a row.

The benchmark gap is small; the price gap is not

Lined up, the SWE-bench numbers run 73.3% to 79.6% to the low 80s. That is a gentle slope. The cost curve is a cliff: Haiku to Sonnet is roughly 3x, Sonnet to Opus is roughly 1.7x, and Haiku to Opus is about 5x end to end.

Two things keep the cost math honest in both directions:

Prompt caching (up to 90% savings) and batch processing (50% savings) apply across all three tiers, so disciplined caching shrinks the absolute dollar gaps.
Cheap-but-wrong is the most expensive outcome. If Haiku needs three tries to land something Sonnet gets in one, you spent more tokens and more of your own time. The savings are only real when the cheaper tier actually clears the task.

The setup most teams land on

The most cost-effective 2026 Claude setups do not pick a model. They layer the ladder:

Haiku 4.5 as the front line: routing, classification, completions, boilerplate, summaries, the high-frequency shallow tail.
Sonnet 4.6 as the workhorse: the bulk of code generation, refactoring, review, and debugging.
Opus 4.8 held in reserve: the multi-file, architecture-level, and long-agent problems that justify the premium.

Done well, this is the whole game. You spend Opus money only on Opus-grade problems and Haiku money on everything that does not care.

How to access Opus, Sonnet, and Haiku

haimaker.ai — reach all three tiers, plus hundreds of other models, through one OpenAI-compatible endpoint with unified pricing and benchmarks, so you can route between Haiku, Sonnet, and Opus without juggling separate accounts.
Anthropic’s API — all three are first-party; export ANTHROPIC_API_KEY and call them directly.
In a coding agent — every tier is a drop-in choice for OpenClaw. See best Claude models for OpenClaw for the full setup, and the Haiku 4.5 vs Sonnet 4.6 comparison for a closer look at the two cheaper tiers.

COMPARE CLAUDE MODELS

The bottom line

Sonnet 4.6 is the right default for almost everyone. Haiku 4.5 is fast and cheap enough that you should hand it everything shallow, and Opus 4.8 is strong enough to be worth holding back for the problems that actually need it. The question was never “which one.” It is “where do the lines fall in my work,” and the savings live in drawing them well.