Glossary

Claude Sonnet

What is Claude Sonnet?

Claude Sonnet is the middle of Anthropic's three Claude model families, combining strong capability with short response times and moderate cost. It suits a wide range of everyday business tasks: drafting texts, analysing documents, answering knowledge questions or writing code. Anthropic releases new versions regularly; the current one is Claude Sonnet 5.5 (as of October 2026).

How KI.ordo helps

In KI.ordo, Sonnet is one of the Claude models available for agents and AI steps; the cost per model is shown in the usage overview.

Learn more →: Claude Sonnet

Topics

  • Claude
  • Anthropic
  • model choice

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Related terms

Claude

Claude is a family of large language models from the US AI company Anthropic. The models understand and write text in many languages, analyse documents, write code and can use tools, for example for research or automation. Best known are the model families Haiku, Sonnet and Opus, which differ in speed, cost and capability. Anthropic places particular emphasis on safety and reliable model behaviour.

  • Anthropic
  • language model
  • +1

Claude Haiku

Claude Haiku is the fastest and least expensive of Anthropic's three Claude model families, alongside Sonnet and Opus. It suits high-volume tasks that need short response times, such as sorting emails, extracting data from texts or routing requests to the right place. The current generation is Claude Haiku 5.5 (as of October 2026).

  • Claude
  • Anthropic
  • +1

Claude Opus

Claude Opus is the most capable of Anthropic's three Claude model families: Haiku, Sonnet and Opus. It is designed for demanding tasks that require multi-step reasoning, such as complex analyses, large programming projects or agents that work independently over longer periods. In return, it is slower and more expensive than the smaller families. The current version is Claude Opus 5.5 (as of October 2026).

  • Claude
  • Anthropic
  • +1

Large language model (LLM)

A large language model (LLM) is a neural network trained on very large amounts of text, enabling it to understand and generate language. It works by calculating the most likely next token, step by step. LLMs write, summarise, translate, answer questions and generate code; well-known examples include Claude, GPT, Gemini and Llama. Without access to current sources, they only know what was in their training data.

  • Language models
  • Generative AI
  • +1