Claude Sonnet 4.6
Anthropic's balanced model for high-quality coding, reasoning, and agentic tool use — with a 1M-token context (beta) and vision.
Claude Sonnet 4.6 is a cloud text model built by Anthropic. 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 and temperature. It runs through Replicate and Runware using your own API key, from $3.00 per million input tokens.
- Pricing
- $3.00 / 1M in · $15.00 / 1M out
- Inputs
- Text, Images
- Context window
- 1M in · 64K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature
Examples
Generated with Claude Sonnet 4.6 via Runware. 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.
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 software programs talk to each other. When you tap "Sign in with Google" on an app, that app uses Google's API to ask Google to confirm who you are, without the app ever seeing your password.
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 provider like Auth0, Clerk, or Supabase Auth. As a two-person team, your scarcest resource is time, and authentication done correctly is genuinely hard — password hashing, session management, MFA, OAuth flows, security patches, and compliance requirements will consume weeks you should be spending on the product that actually differentiates you. Hosted providers handle these problems reliably for pennies per user at early scale, and migrating away later, while annoying, is entirely feasible once you have the team and revenue to justify it. The strongest argument against this is vendor dependency and data control. Your users' identity data lives on someone else's infrastructure, and if that provider raises prices dramatically, gets acquired, or shuts down, you face a potentially painful forced migration at the worst possible time — when you're growing and least able to absorb the disruption.
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.
Forecasting AI expenditure is genuinely difficult, and understanding why helps you make smarter decisions about it. The first challenge is that usage drives cost in ways traditional software does not. Most enterprise software is licensed at a flat rate, but AI systems typically charge per query, per token, or per compute hour. When a tool proves popular internally, costs can multiply overnight without anyone making a deliberate purchasing decision. The second problem is model churn. AI capabilities are improving so rapidly that the tools you evaluate today may be obsolete within months. Teams frequently switch vendors or upgrade to newer models, each transition carrying hidden integration and retraining costs that rarely appear in initial projections. Third, experimentation is structurally unpredictable. Unlike conventional IT projects with defined scopes, AI adoption tends to sprawl. Engineers prototype, business units pilot their own solutions, and shadow spending accumulates before finance even becomes aware of it. What can you do? Start by treating AI spending as a utility budget, building in meaningful variance buffers rather than fixed line items. Establish a central registry requiring teams to log AI tools before adoption, giving you visibility before costs arrive. Finally, set usage alerts and spending caps at the account level so surprises are caught early rather than discovered in quarterly reviews.
Anthropic
Anthropic builds the Claude family of large language models, focused on safety, helpfulness, and honesty.
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