Claude Sonnet 5.5
Claude Sonnet 5.5 is Anthropic's middle tier, pitched as a balance of speed and intelligence.
Specifications & pricing.
- FIELD
- DOCUMENTED VALUE
- SOURCE
- Input / output modalities
Text and images in; text out
Sonnet 5.5 model page
- Context window
1M tokens
Sonnet 5.5 model page
- Max output
128K tokens; up to 300K on the Message Batches API with a beta header
Sonnet 5.5 model page
- Knowledge cutoff
Reliable knowledge cutoff June 2026; training data cutoff June 2026
Sonnet 5.5 model page
- Thinking and effort
Adaptive thinking on by default; lowest setting
between_tools; default efforthighSonnet 5.5 model page
- Input / output price
USD 2 / USD 10 per 1M tokens (list price as published on 15 October 2026)
- Prompt caching
5 minute write USD 2.50, 1 hour write USD 4, cache hit USD 0.10 per 1M tokens
Anthropic pricing
- Batch API
USD 1 input / USD 5 output per 1M tokens (50% discount)
Anthropic pricing
- Platforms
Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Sonnet 5.5 model page
- Lifecycle
Released 28 September 2026; Active; retirement not sooner than 28 September 2027
When to use it
What Anthropic says. The choosing guide's selection matrix lists Sonnet 5.5 for "speed and capability for everyday coding, agent, and enterprise workloads", with code generation, data analysis, content creation, visual understanding and agentic tool use as examples. The guide says to start at the default high effort and adjust based on your evals. The deprecations page names claude-sonnet-5-5 as the recommended replacement for Claude Sonnet 4.5, which retires on 30 November 2026, and for several older Sonnet models.
Our editorial judgment. Sonnet 5.5 looks like the natural production tier for teams that have prototyped on Opus 5.5 and now need to control spend. The option to turn off up-front thinking is useful for interactive tools where response time matters more than depth. For short, repetitive jobs such as tagging or routing, Claude Haiku 5.5 is listed at a fraction of the price and is worth testing first. If you are picking a packaged coding assistant rather than an API model, see our AI coding tools guide.
Limits and costs to plan for
- Five breaking changes from Claude Sonnet 5. The model page lists: turning off up-front thinking now uses
between_tools; forced tool use returns an error; thinking blocks are tied to the model and conversation; on the Claude API and Google Cloud the earliercomputer_20251124tool is not accepted; and the advisor tool rejects Claude Opus 4.8, Claude Opus 4.7, Claude Sonnet 5 and Claude Haiku 5.5 as advisors. between_toolshas an effort ceiling. The lowest thinking setting works only athigheffort or below.- Quiet streams between tool calls. Text between tool calls comes back in
thinkingblocks. Apps that stream that text go quiet until they set adisplayvalue or switch tobetween_tools. - Sampling parameters. Setting
temperature,top_portop_kto a non-default value returns a 400 error. - No fast mode. The pricing page lists fast mode only for Opus 5.5, Opus 5 and Opus 4.8.
- US-only inference costs more. Setting
inference_geoto"us"applies a 1.1x multiplier to all token prices for Claude 4.6 and later models. - Tokenizer. Anthropic says Claude 4.7 and later models produce about 30% more tokens for the same text than earlier models.
How it compares within the family
All four current models share a 1M token context window, 128K max output, text and image input and a June 2026 reliable knowledge cutoff, according to the models overview.
| Model | Input / output (USD per 1M tokens) | Cache hit | Latency | Thinking | Default effort |
|---|---|---|---|---|---|
| Claude Fable 5.1 | 10 / 50 | 0.25 | Slower | Adaptive, always on | high |
| Claude Opus 5.5 | 4 / 20 | 0.20 | Moderate | Adaptive, always on | medium |
| Claude Sonnet 5.5 | 2 / 10 | 0.10 | Fast | Adaptive | high |
| Claude Haiku 5.5 | From 0.10 / 0.50 | From 0.01 | Fastest | Adaptive | medium |
Against its predecessor, the pricing page lists Claude Sonnet 5 at the same USD 2 input and USD 10 output, but with cache hits at USD 0.20 per 1M tokens, double the Sonnet 5.5 rate. Claude Sonnet 4.6 and 4.5 are listed at USD 3 input and USD 15 output.
How to evaluate it on your work
- Take 20 to 50 tasks with known good outputs, including some long tool-using runs.
- Run them on Sonnet 5.5 at the default
higheffort and on Opus 5.5 atmedium, with identical prompts and tools, scoring each against your own rubric. - For interactive features, rerun the set with
between_toolsand record latency alongside pass rate. - Multiply logged input, cached and output tokens by list prices to get cost per completed task, and move a task type to Sonnet 5.5 only where its pass rate holds up.
Method and limits
We read Anthropic's models overview, Sonnet 5.5 model page, pricing page, choosing a model guide and deprecations page on 15 October 2026. We have not run Claude Sonnet 5.5, and nothing here is a test result or ranking. Check the vendor pages before committing budget. See our ranking method for how HumanRank scores tools and the model directory for other profiles.
Frequently asked questions
What is Claude Sonnet 5.5? It is Anthropic's mid-tier model, positioned as a combination of speed and intelligence for everyday coding, agent and enterprise workloads.
How much does Claude Sonnet 5.5 cost? It costs USD 2 per 1M input tokens and USD 10 per 1M output tokens at list price on 15 October 2026, with cache hits at USD 0.10 per 1M tokens.
What is the Claude Sonnet 5.5 context window?
It is 1M tokens, with up to 128K output tokens, or 300K on the Message Batches API with the output-300k-2026-03-24 beta header.
Can I turn off thinking on Claude Sonnet 5.5?
Partly, the between_tools setting turns off up-front thinking, and it works at high effort or below.
Does Claude Sonnet 5.5 replace Claude Sonnet 4.5? Yes, Anthropic names it as the recommended replacement for Claude Sonnet 4.5, which is deprecated and retires on 30 November 2026.
Is Sonnet 5.5 cheaper than Opus 5.5? Yes, its list token prices are half of Opus 5.5's, though whether it costs less per finished task depends on your own test results.