Claude Haiku 5.5 vs Sonnet 5.5 vs Opus 5.5: Which Should You Use?

If you want cheap, fast responses for high-volume tasks like classification, extraction, routing, or acting as a subagent inside a coding workflow, use Claude Haiku 5.5. If you want the best all-around balance of speed and intelligence for everyday work, use Claude Sonnet 5.5. If you're running long, demanding agentic coding or knowledge-work sessions where accuracy matters more than cost, use Claude Opus 5.5. That's the short version of Anthropic's own guidance, and it holds up once you look at the pricing, context windows, and benchmark gaps between the three models.

Anthropic introduced Claude Haiku 5.5 on October 7, 2026, describing it as "our fastest, most capable small model," built for high-volume work like summarization, subagents, and browser use. It arrived a little over a week after Claude Sonnet 5.5 (September 28) and about two weeks after Claude Opus 5.5 (September 22), completing a refresh of Anthropic's entire mainline lineup inside a single month. Despite that, Haiku 5.5 hadn't been covered on Pandromeda until now — so this is the catch-up piece, laid out as a straight comparison of what each model costs, how it performs, and who it's actually for.

What is Claude Haiku 5.5?

Claude Haiku 5.5 is the small, high-volume member of Anthropic's current model family. Anthropic's own comparison table on the Claude Platform models overview page describes it as built "for high-volume, latency-sensitive tasks such as classification, extraction, and routing," with "Fastest" comparative latency across the current lineup. Anthropic also positions it as a subagent that can run alongside Opus 5.5 or Sonnet 5.5 during coding work, and as the model behind speed-sensitive products like live customer support and browser-use agents.

Unlike earlier Haiku generations, Haiku 5.5 is the first Haiku-class model with an adjustable effort setting (Low, Medium, High, Xhigh, Max), letting developers trade latency for more careful reasoning on a given request, and it uses adaptive thinking by default, the same mechanism Sonnet 5.5 and Opus 5.5 use to decide on their own how much to "think" before answering.

Pricing compared: Haiku 5.5 vs Sonnet 5.5 vs Opus 5.5

This is where the three models separate most sharply. Anthropic's pricing is published on claude.com/pricing and on the Claude Platform docs, and it holds a genuine surprise: Haiku 5.5 is not flat-rate. It uses a two-tier structure based on prompt length.

For prompts under 100,000 tokens — the vast majority of everyday requests — Haiku 5.5 costs roughly 20 times less per input token than Sonnet 5.5 and 40 times less than Opus 5.5. Anthropic says this works out to about 75% lower cost on average than its predecessor, Claude Haiku 4.5, with the company's footnotes breaking that down further: a 90% cut for requests up to 100,000 tokens and a smaller, 50% cut above that threshold, where pricing jumps five-fold across input, output, and both cache rates. Prompt caching read rates follow the same split: $0.01 per million tokens for Haiku 5.5 reads under the 100K line versus $0.10 for Sonnet 5.5 and $0.20 for Opus 5.5.

All three models are available through the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry under pinned model IDs (claude-haiku-5-5, claude-sonnet-5-5, claude-opus-5-5), plus the usual batch discount (50% off) and a US-only inference option at a 1.1x surcharge.

Context window and output limits

Here's a detail that surprises people who remember older Haiku models as the "limited" tier: Claude Haiku 5.5 now shares the same 1-million-token context window and 128,000-token maximum output as both Sonnet 5.5 and Opus 5.5, according to Anthropic's models overview documentation. That's a meaningful jump from Haiku 4.5's 200,000-token window. In practical terms, Anthropic notes that 1 million tokens is roughly 555,000 words on the tokenizer introduced with Claude Opus 4.7 and used across the current generation.

So the three models are no longer differentiated by how much text they can hold in memory — all three can ingest a similarly huge amount of context. The differentiation is entirely about speed, reasoning depth, and price per token, not capacity.

Speed and real-world performance

Anthropic's own comparative-latency labels on the models overview page rank the lineup as: Haiku 5.5 "Fastest," Sonnet 5.5 "Fast," and Opus 5.5 "Moderate" (with the larger Claude Fable 5.1 reasoning model ranked "Slower" still). That ordering lines up with how each model is marketed — Haiku for latency-sensitive production traffic, Sonnet as the default workhorse, Opus for sessions where depth matters more than response time.

Anthropic's Haiku 5.5 announcement page also published head-to-head benchmark figures against Haiku 4.5 and Sonnet 5.5. On Terminal-Bench 4.0, a benchmark for autonomous terminal/agentic tasks, Anthropic reported Haiku 5.5 scoring 39.2%, up from 0.0% for Haiku 4.5, while Sonnet 5.5 scored 70.6%. On OSWorld 2.1 (an offline computer-use subset), Haiku 5.5 reached 72.4% against 15.7% for Haiku 4.5 and 83.9% for Sonnet 5.5. The pattern repeats across Anthropic's other reported evaluations: Haiku 5.5 closes much of the gap to its predecessor's weaknesses without fully catching up to Sonnet 5.5, which is exactly the trade-off its pricing implies.

Anthropic also cited customer-reported results it says are not independently verified — for example, a logistics/CRM customer measuring over 30% faster task completion compared with its prior model, and a data-analysis customer reporting a higher internal evaluation score than Haiku 4.5 at similar or lower latency. Treat those as vendor-reported case studies rather than independent benchmarks.

Which model for which job

Based on Anthropic's own positioning language across its model pages, here's how the three break down by task:

• Claude Haiku 5.5 — high-volume, latency-sensitive, repetitive work: classification, data extraction, routing, summarization/compaction, live customer support, browser-use agents, and acting as a fast, cheap subagent that a Sonnet 5.5 or Opus 5.5 "orchestrator" delegates smaller steps to during a coding or research session.
• Claude Sonnet 5.5 — Anthropic calls it "the best combination of speed and intelligence" on its models overview page, and it's the model most people should default to for everyday writing, analysis, and coding tasks that need more judgment than Haiku but don't require Opus-level depth. If you're deciding between Sonnet 5.5 and the other two, Pandromeda's explainer on Sonnet 5.5's pricing, speed, and availability goes deeper on that specific model.
• Claude Opus 5.5 — long-running agentic coding and knowledge work, where Anthropic's documentation says it's suited to demanding, extended sessions rather than quick single-turn replies. For a full rundown of pricing and release details, see Pandromeda's Claude Opus 5.5 explainer.

Worth noting: Anthropic's current "choose a model" guidance actually recommends starting with Opus 5.5 for most workloads by default, reserving its separate, larger Fable 5.1 reasoning model for cases where even high-effort Opus 5.5 falls short. Haiku 5.5 and Sonnet 5.5 are the models you reach for deliberately, when cost or latency — not raw capability — is the binding constraint.

API, Claude Code, and app availability

All three 5.5 models are available through the Claude API, Amazon Bedrock, Google Cloud's Vertex AI, and Microsoft Foundry, using the same model ID across platforms (e.g., claude-haiku-5-5). Where they differ is on Anthropic's consumer plans. According to claude.com's pricing page, Claude's free plan includes Sonnet and Haiku but not Opus; Opus is reserved for paid Pro and Max tiers (both the 5x and 20x Max options), where all three models — Haiku, Sonnet, and Opus — are available. Developers building agents can also route Haiku 5.5 as a subagent inside coding tools, where it functions as a fast, cheap worker model underneath a Sonnet or Opus "lead" model handling the harder reasoning steps.

Safety posture and other differences

Anthropic's Haiku 5.5 announcement also addresses safety positioning directly: the company says alignment testing shows major improvements over Haiku 4.5, with fewer misaligned behaviors observed. Its cybersecurity safeguards are described as stricter than Haiku 4.5's but somewhat looser than Sonnet 5.5's — Haiku 5.5 still blocks penetration-testing-style and other attacker-oriented techniques. Biology-related safeguards are described as matching those on Sonnet 5, Sonnet 5.5, and Opus 5. One more under-the-hood change: Haiku 5.5 ships with an updated tokenizer, so it can use slightly more tokens than Haiku 4.5 to represent the same text — worth factoring in if you're estimating costs by word count rather than actual token count.

How this fits into Anthropic's broader 5.5 refresh

Haiku 5.5's release closes out a fast sequence of updates: Opus 5.5 on September 22 (which Anthropic said runs at 40% lower cost than Opus 5 on typical workloads), Sonnet 5.5 on September 28 (pitched as 30%-plus faster and up to 30% cheaper than Sonnet 5), and Haiku 5.5 on October 7. All three now share the same 1-million-token context window and 128K max output, a consistency that wasn't true of the previous generation, where Haiku trailed with a smaller window. If you're also comparing Anthropic's frontier option against competitors, Pandromeda's Opus 5.5 vs. Fable 5.1 comparison covers how Anthropic's two largest models stack up against each other.

What next

If you're choosing today: start with Sonnet 5.5 for general-purpose use unless you have a specific reason to move. Switch to Haiku 5.5 once you've identified a high-volume, repetitive, or latency-sensitive slice of your workload — classification, extraction, summarization, or a subagent role — where its sub-100K-token pricing will meaningfully cut costs. Reach for Opus 5.5 when a task is long-running, high-stakes, or requires deeper reasoning than Sonnet reliably delivers, and you're less concerned about per-token cost than about getting it right. Because pricing for Haiku 5.5 depends on prompt length, it's worth checking your typical token counts against the 100,000-token threshold before assuming it's automatically the cheap option for your use case — and keep an eye on Anthropic's pricing documentation, since per-platform and cache rates can shift independently of the headline numbers.

Frequently asked questions

When was Claude Haiku 5.5 released? Anthropic announced Claude Haiku 5.5 on October 7, 2026.

Is Claude Haiku 5.5 cheaper than Sonnet 5.5 and Opus 5.5? Yes, substantially so for prompts under 100,000 tokens, where it costs $0.10 per million input tokens and $0.50 per million output tokens, versus $2/$10 for Sonnet 5.5 and $4/$20 for Opus 5.5. Above 100,000 tokens, Haiku 5.5's rates rise to $0.50/$2.50, narrowing but not eliminating the gap.

Do Haiku 5.5, Sonnet 5.5, and Opus 5.5 have the same context window? Yes. Anthropic's documentation lists a 1-million-token context window and 128,000-token maximum output for all three models.

Is Claude Haiku 5.5 available on the free Claude plan? According to claude.com's pricing page, Claude's free plan includes Sonnet and Haiku models but not Opus, which requires a paid Pro or Max plan.

What is Claude Haiku 5.5 best used for? Anthropic positions it for high-volume, latency-sensitive tasks: classification, data extraction, routing, summarization, live customer support, browser-use agents, and as a fast subagent underneath Sonnet 5.5 or Opus 5.5 in coding workflows.

Which Claude model should most people use by default? Anthropic's own "choosing a model" guidance recommends starting with Opus 5.5 for most workloads, while Sonnet 5.5 is positioned as the best balance of speed and intelligence for everyday work, and Haiku 5.5 is reserved for cost- or latency-sensitive use cases specifically.

Written byTheo Park

Theo Park runs the AI desk at Pandromeda. He follows model launches from the frontier labs and the open-weight community, tracks the assistants and developer tools built on them, and explains what each release changes on pricing, capability and safety. His reporting leans on primary sources: model cards, technical reports, API documentation and the companies' own announcements.

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