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Text · Released Jun 2026

Anthropic Claude Sonnet 5

Anthropic's most agentic Sonnet — frontier-level coding and tool use at Sonnet speed and price, with vision.

From Anthropic, Claude Sonnet 5 is a cloud text model. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. Its context window handles up to 1M input tokens and up to 64K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions. It runs through Replicate, OpenRouter, and Anthropic using your own API key, from $2.00 per million input tokens.

Modality
Text
Model ID
anthropic/claude-sonnet-5
Specs
Released
3 months ago (Jun 2026)
Pricing
$2.00 / 1M in · $10.00 / 1M out
Inputs
Text, Images
Context window
1M in · 64K out
Reasoning
Adjustable effort
Controls
System prompt
Samples

Examples

Generated with Claude Sonnet 5 via Replicate. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.

Short answer48 / 50 words

Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.

An API is a set of rules that lets two computer programs talk to each other. One program offers certain services or information, and the API defines exactly how another program can request them—what to ask for, how to ask, and what kind of answer to expect back.

Medium answer150 / 150 words

A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.

Use a hosted authentication provider (like Auth0, Clerk, Supabase Auth, or Firebase Auth). For a two-person team, engineering time is the scarcest resource you have, and authentication is a solved problem with enormous security surface area—password resets, session management, MFA, OAuth flows, token security, compliance requirements. Building this yourselves means spending weeks on undifferentiated infrastructure instead of your actual product, and any subtle bug in a homegrown auth system can lead to a catastrophic breach that kills a young company's reputation instantly. Hosted providers have dedicated security teams, handle edge cases you haven't thought of, and let you ship auth in days rather than weeks. The strongest counterargument: hosted auth creates a critical dependency and recurring cost that scales with users, and migrating away later—once you're deeply integrated and have thousands of accounts—can be far more painful and risky than if you'd built on flexible, self-owned infrastructure from the start.

Long answer205 / 190–220 words

Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.

AI spending is difficult to forecast for reasons that differ fundamentally from traditional IT costs, and it's worth understanding why before trying to budget around it. First, usage-based pricing means costs scale with demand rather than following a fixed subscription. Unlike a software license with a predictable annual fee, AI tools often charge per query or per token processed, so a successful product feature or a popular internal tool can quietly multiply your bill as adoption grows. Second, the technology itself is a moving target. Vendors regularly release new models with different pricing, and teams frequently switch or upgrade to stay competitive, which resets cost assumptions every few months rather than every few years. Third, usage patterns are inherently unpredictable because employees and customers are still discovering what these tools are good for. A chatbot that seems lightly used can suddenly become central to a workflow, driving costs up fast and with little warning. To manage this, set spending caps and alerts rather than fixed budgets, review usage monthly instead of annually, and require teams to justify scaling up a given use case before they do. Treat AI costs as a variable, actively monitored expense, similar to cloud computing, rather than a predictable line item.

About the creator

Anthropic

Anthropic builds the Claude family of large language models, focused on safety, helpfulness, and honesty.

www.anthropic.com ↗
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