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Claude Sonnet 5

Claude Sonnet 5

5 · Family: Claude
The most agentic Sonnet model to date — performance close to Opus 4.8 at a lower price. Released June 30, 2026; 1M-token context; API identifier: claude-sonnet-5.
✓ Public accessFeaturedLLMReasoning modelMultimodal📁 Claude
Context window
1 mln (1M) tokenów (kontekst rodziny Sonnet od wersji 4)
tokens
Parameters
Niepublikowane przez Anthropic (firma nie ujawnia liczby parametrów modeli Claude).
parameters
Max output
64,000
tokens
Release date
30 June 2026
Access:APIHostedDeployment:☁ Cloud

Overview

Claude Sonnet 5 is the most agentic Sonnet-class model to date — released by Anthropic on June 30, 2026 as the direct successor to Sonnet 4.6 (February 2026). It can plan, use tools like browsers and terminals, and run autonomously at a level that, just a few months ago, required larger and more expensive models.

Position in Anthropic's portfolio

The Sonnet line started the agentic-AI era for developers (Sonnet 3.5, 3.6, 3.7 were the first models with impressive coding and tool-use skills). Recent clear gains in agentic capabilities came mostly from the Opus class — Sonnet 5 closes that gap: performance close to Opus 4.8 at lower prices. Sonnet 5 is the default model for Free and Pro plans; also available for Max, Team and Enterprise as well as in Claude Code and on the Claude Platform.

Key improvements

Substantial gains over Sonnet 4.6 in: reasoning, tool use, coding, and knowledge work. On the BrowseComp (agentic search) and OSWorld-Verified (computer use) agentic evaluations, Sonnet 5 is a strict improvement over Sonnet 4.6, and the configurable 'effort' level lets you balance cost and quality per request. A new tokenizer (as in Opus 4.7) changes the text-to-token mapping at a 1.0-1.35× ratio versus Sonnet 4.6 — introductory pricing has been set so the transition to Sonnet 5 is cost-neutral.

Safety

Pre-deployment safety: overall lower rates of misaligned behavior than Sonnet 4.6, though higher than Opus 4.8 and Mythos Preview. Fewer hallucinations and less sycophancy. Cyber: on software-exploit development evaluations (e.g. Firefox 147) Sonnet 5 was never able to develop a working exploit; slightly higher partial-success rate than Sonnet 4.6 — a side effect of general intelligence improvements rather than targeted training. Cyber safeguards enabled by default (the same as in Opus 4.7/4.8).

Pricing and availability

Introductory pricing (through August 31, 2026): $2/M input tokens and $10/M output tokens. Standard pricing (from September 1, 2026): $3/M input and $15/M output. Prompt caching: up to 90% savings; batch processing: 50% savings. US-only inference: 1.1× standard price. Availability: claude.ai (web/iOS/Android), Claude Code, Claude Platform, AWS Bedrock, Google Cloud Vertex, Microsoft Foundry.

Classification
LLMReasoning modelMultimodal
Family: Claude
Access & deployment
APIHosted
Cloud
Weights: Closed
Key parameters
📏 Context: 1 mln (1M) tokenów (kontekst rodziny Sonnet od wersji 4)
🧩 Parameters: Niepublikowane przez Anthropic (firma nie ujawnia liczby parametrów modeli Claude).
Tools
📥 Input: text, image, documents

Technical specification

Context window
1 mln (1M) tokenów (kontekst rodziny Sonnet od wersji 4)
tokens
Parameters
Niepublikowane przez Anthropic (firma nie ujawnia liczby parametrów modeli Claude).
parameters
Max output tokens
64,000
tokens per response
Knowledge cutoff
1 Jul 2025
Knowledge boundary
License
Proprietary — dostęp przez Claude API, claude.ai i partnerów chmurowych (AWS, GCP, Azure/Microsoft Foundry).
Hardware requirements
Available exclusively via Anthropic's cloud API; no self-hosting. Inference runs on Anthropic's own infrastructure and on partner clouds: AWS (Trainium/Inferentia + Bedrock), Google Cloud (TPU/Vertex), Microsoft Foundry (Azure).
Features:Tool use
Modalities
⬇ Input
textimagedocuments
⬆ Output
textcodestructured_data

Capabilities and applications

Native model capabilities
Coding
Generating, analysing and modifying source code.
Category: coding
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Agentic capability
The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.
Category: planning
Function Calling
Category: planning
Long context
Maintaining coherence and focus across very long input context.
Category: language
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Computer use
The model's ability to operate a computer interface by interpreting screenshots and generating actions such as clicks, typing, and navigating applications.
Category: planning
Planning
Forming and executing action plans for complex tasks.
Category: planning
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Multilingual
Understanding and generating text in many languages.
Category: language
Vision encoder
The model's ability to encode images and video frames into dense representations (embeddings), used for downstream tasks or as a backbone for vision-language models.
Category: vision
Image understanding
Analysing and interpreting the content of images.
Category: vision
Application domains

Technical architecture

Core Architecture