Claude Haiku vs Sonnet: Which One to Actually Use

As of September 2026, Claude Haiku vs Sonnet means Claude Haiku 4.5 against Claude Sonnet 5. The two sit a generation apart in naming — Haiku 4.5 shipped in October 2025, Sonnet 5 on June 30, 2026 — so it's easy to end up comparing mismatched versions.

Four differences settle almost every real choice:

• Price. Sonnet 5 costs twice what Haiku 4.5 does per token, on input and on output.
• Context window. Haiku 4.5 holds 200K tokens. Sonnet 5 holds 1M.
• How you control thinking. Sonnet 5 uses adaptive thinking steered by an effort setting. Haiku 4.5 uses manual extended thinking with a token budget, and has no effort parameter.
• Knowledge. Sonnet 5's reliable knowledge runs to January 2026. Haiku 4.5's stops at February 2025.

If the job fits in 200K tokens, doesn't need long chains of reasoning, and runs at volume, Haiku 4.5 is the sensible default. Cross any one of those lines and Sonnet 5 earns the extra cost.

Claude Haiku vs Sonnet at a glance

Everything in that table comes from Anthropic's models overview, and the prices match the pricing page as of September 11, 2026. One recent change is worth knowing: Sonnet 5 launched at $2 / $10 as introductory pricing through August 31, 2026, and the pricing page now lists that as its standard price, with the planned September increase called off. Check the page before you budget, because that's exactly the kind of thing that moves.

The Batch API halves both models' prices, and cache reads cost a tenth of base input on both, so the 2× gap holds across discounts too. We cover how those discounts combine in how to get Claude free API access.

"Claude 4 Haiku" is Claude Haiku 4.5

There's no model called Claude 4 Haiku or Claude Haiku 4. Anthropic's deprecations table goes straight from Claude Haiku 3.5 (retired in February 2026) to Claude Haiku 4.5, which is what people usually mean by "Claude 4 Haiku." Its dated ID is claude-haiku-4-5-20251001, and the alias claude-haiku-4-5 points at the same snapshot.

One date to note if you're building something long-lived on it: the same table lists Haiku 4.5 as Active, with a tentative retirement of not sooner than October 15, 2026. No deprecation has been announced, and Anthropic commits to at least 60 days' notice before retiring a publicly released model. That's not a reason to avoid it. It is a reason to keep your model ID in config rather than hard-coded, and to glance at that page occasionally.

Where the context window decides it

200K tokens sounds enormous until you feed it real material.

• A long document, a conversation about it, and a few tool results fit comfortably in 200K.
• A mid-sized codebase, a few hundred pages of PDFs, or a multi-hour agent session that keeps piling up tool output is where 200K runs out.

What happens at the limit depends on where you're running. On the raw API, a request whose input already exceeds the window gets a 400 "prompt is too long" error, per the context windows documentation. Anthropic's server-side compaction (in beta) summarises older context to keep a long conversation going. Its supported-model list includes Sonnet 5 and doesn't include Haiku 4.5, so on Haiku you manage that yourself.

In Claude Code the picture is different again: sessions auto-compact before the window fills, and Anthropic's Claude Code docs say Sonnet 5 always runs at 1M there when connected to the Anthropic API, with no [1m] variant to pick and no usage credits needed on any plan. Claude Code's 1M context window covers which models and plans get it, and what survives auto-compact covers what you lose when a session summarises itself.

Output is the smaller gap but a real one: 64K tokens per response on Haiku 4.5 against 128K on Sonnet 5. If you generate long structured documents in one pass, that's a ceiling you'll hit. Sonnet 5 can also go to 300K output tokens on the Message Batches API with a beta header; Haiku 4.5 isn't on that list.

Where thinking and effort decide it

Both models can think before answering. They're steered differently.

Haiku 4.5 uses manual extended thinking: you turn it on and set budget_tokens, a cap on how much it can think. That's a real control, but it only governs thinking.

Sonnet 5 uses adaptive thinking, where the model decides how much to think, steered by the effort parameter. Effort shapes more than thinking. Anthropic's docs say it also affects tool calls and the explanations around them. Sonnet 5 defaults to high, and Anthropic's guidance for it is:

• high (the default) for complex reasoning, coding, and agentic work where quality matters more than speed
• xhigh for the hardest coding and agentic tasks
• low for high-volume or latency-sensitive workloads such as chat

That last line matters for this comparison. Sonnet 5 at low effort is Anthropic's own suggestion for exactly the high-volume, latency-sensitive work people usually hand to Haiku. It still costs twice as much per token, so it won't beat Haiku on price, but it gives you a middle option before you commit to either model. Anthropic's guidance on choosing a model puts it plainly: tuning effort is often a better lever than switching models.

The knowledge cutoff nobody checks

Haiku 4.5's reliable knowledge cutoff is February 2025. Sonnet 5's is January 2026. That's an eleven-month gap.

For most work it doesn't matter. Summarising a document, classifying a ticket, or pulling fields out of an invoice doesn't depend on recent knowledge. It matters when the task touches a fast-moving library, a framework's current API, or anything that happened after early 2025, because an older model answers confidently from what it knew.

The cheaper fix is usually context, not a bigger model. Paste the current docs into the prompt, or give the model a search tool, and a model with an older cutoff can still work from the right material.

How to pick without guessing

Anthropic describes two starting strategies, and both beat picking by reputation.

Efficiency-first. Build on Haiku 4.5, test your use case thoroughly, and upgrade only where you find a specific capability gap. Anthropic recommends this for prototyping, tight latency requirements, cost-sensitive builds, and high-volume straightforward tasks.

Capability-first. Build on the strongest starting point (Anthropic names Opus 5), tune your prompts there, then reduce effort or step down to cheaper models once you know the task works. This suits complex work where you don't yet know whether the job is achievable at all.

Anthropic's model selection matrix gives each model a lane. Its example use cases, plus the context-window line from earlier, make a reasonable starting allocation:

Neither is Anthropic's top pick for general workloads, though. Its overview suggests starting with Claude Opus 5 for most work, and Claude Fable 5.1 for demanding reasoning and long-horizon agentic tasks. If your real question is about writing software, which Claude model is best for coding walks through that lineup and the effort settings that go with it.

Four questions that settle it

Run your use case through these in order:

1. Will input plus history ever pass 200K tokens? If yes, Sonnet 5.
2. Does it need multi-step reasoning or long agent loops? If yes, Sonnet 5 — and try its effort dial before anything bigger.
3. Does it depend on anything after early 2025? If yes, either give Haiku 4.5 the current material or use Sonnet 5.
4. Is it high-volume and latency-sensitive? If yes, start on Haiku 4.5 and measure, since it's half the price and the fastest in the lineup.

If you answered "no, no, no, yes," Haiku 4.5 is your model. Any "yes" in the first three usually means Sonnet 5.

Settling on a model tends to be the quick part; what gets lost is everything around it, like the tuned prompt, the tool setup, and the test set that told you Haiku was good enough. Taku lets you mirror an AI setup someone has already tuned into a desktop workspace and keep running it, so that work stays with you instead of dying in one chat. Taku is in Beta, and the Mac app is available now.