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Claude Opus 5.5: Pricing, Benchmarks & Features

Philip Moses
Sep 23
3 min read

Anthropic has introduced Claude Opus 5.5, the first model in its new 5.5 generation.

The main focus of this release is not simply higher benchmark scores. Opus 5.5 is designed to deliver strong performance at a lower cost, with faster output and stronger safety measures.


In this article, we’ll look at what is new, how Opus 5.5 performs, its pricing, and what the release means for developers and businesses.

What Is Claude Opus 5.5?

Claude Opus 5.5 is Anthropic’s latest flagship model, built for demanding workloads such as:

  • Agentic coding

  • Complex research

  • Data analysis

  • Multi-step workflows

  • Computer-use tasks

Compared with Opus 5, the focus is on better efficiency. Anthropic says the model can handle similar workloads while using fewer resources, reducing both cost and response time.

What’s New in Claude Opus 5.5?

Lower Cost and Faster Output

One of the biggest changes is pricing.

Compared with Opus 5:

  • Input tokens: $5 → $4 per million

  • Output tokens: $25 → $20 per million

  • Cache reads: $0.50 → $0.20 per million

  • Cache writes: $6.25 → $5 per million

Anthropic also reports that Opus 5.5 generates output more than 30% faster than Opus 5.

Better Performance on Long Tasks

Opus 5.5 is particularly focused on long-running, complex tasks rather than only short benchmark questions.

This is important for AI coding agents and other workflows where a model needs to work through many steps without losing context or consistency.


More Concise Communication

Anthropic also highlights improvements in how Opus 5.5 communicates.

The model is designed to:

  • Lead with important information

  • Reduce unnecessary verbosity

  • Use clearer language

  • Follow a company's preferred writing style more closely

This can make generated explanations and technical responses easier to use.

Claude Opus 5.5 Benchmark Results

Here's how Opus 5.5 stacks up against Fable 5.1, Opus 5, and the competition, per Anthropic's own release.


The results show particularly strong performance in agentic coding and computer-use tasks.

However, performance varies by benchmark. For example, GPT-6 Astra scores higher on AutomationBench and Terminal-Bench-Science in the comparison provided by Anthropic.

Claude Opus 5.5 Pricing

Opus 5.5 is priced lower than Opus 5 across both standard input and output tokens.

Per 1M Tokens

Opus 5.5

Opus 5

Input

$4

$5

Output

$20

$25

Cache reads

$0.20

$0.50

Cache writes

$5

$6.25

Anthropic also offers Fast mode, which can provide up to 2.5× faster processing at a higher token price.

Claude Opus Pricing Over Time

The pricing trend also shows how Anthropic's flagship models have become less expensive over successive releases.



This chart works best immediately after the pricing table, because readers can first see the current Opus 5.5 pricing and then understand how it compares with earlier Opus generations.

Safety and Availability

Anthropic says Opus 5.5 includes safeguards covering areas such as:

  • Cybersecurity

  • Biology

  • Model distillation

The system can route certain flagged requests to different models rather than simply relying on output filtering.

Anthropic also reports that Opus 5.5 underwent external evaluation before release.

The model is available through Anthropic's platforms as well as Amazon Web Services, Google Cloud, and Microsoft Azure.

What Does Claude Opus 5.5 Mean for AI Agents?

The biggest change with Opus 5.5 may be its economics.

AI agents often make many model calls while working through a task. Lower input, output, and cache costs can therefore have a significant impact on the total cost of an agent workflow.


Combined with faster generation and strong coding performance, Opus 5.5 is positioned for workloads where AI needs to work through complex tasks repeatedly.

Conclusion

Claude Opus 5.5 focuses on efficiency rather than simply increasing model capability.

With lower pricing, faster output, strong agentic coding performance, and expanded safety measures, it is designed to make advanced AI workflows more practical at scale.

For developers and businesses, the key question is no longer only how capable a model is, but how much useful work it can deliver for the cost.

 
 
 

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