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Claude Opus 4.8

Anthropic's most capable model — frontier reasoning, coding, and agentic tool use with vision.

Claude Opus 4.8 is a cloud text model from 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 128K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions and temperature. It runs through Runware using your own API key, from $5.00 per million input tokens.

Modality
Text
Available on
Model ID
anthropic/claude-opus-4.8
Specs
Pricing
$5.00 / 1M in · $25.00 / 1M out
Inputs
Text, Images
Context window
1M in · 128K out
Reasoning
Adjustable effort
Controls
System prompt, Temperature
Samples

Examples

Generated with Claude Opus 4.8 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.

Short answer40 / 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 programs talk to each other. One program requests something specific, and the other sends back exactly what was asked for, without either needing to know how the other works inside.

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 provider like Auth0, Clerk, or Supabase Auth. As a two-person team, your scarcest resource is engineering time, and authentication is a deep, unglamorous problem full of security landmines: password hashing, session management, token rotation, account recovery, breach detection, and eventually multi-factor and social login. A hosted provider gives you all of this immediately, maintained by specialists who handle vulnerabilities you'll never hear about. Every hour you don't spend building login is an hour spent on the product that actually differentiates you. Ship faster, sleep better. The strongest argument against this: you're handing a critical dependency and your users' identity data to a third party whose pricing, reliability, and continued existence you don't control. Providers have been known to raise prices sharply once you're locked in, and migrating authentication later is genuinely painful. If your economics or scale make that lock-in dangerous, owning it earlier may be wiser.

Long answer213 / 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.

When companies adopt AI tools, spending often proves harder to forecast than traditional software budgets, and understanding why helps you manage it. The first cause is usage-based pricing. Unlike a fixed monthly license, most AI services charge for every query or unit of text processed, so costs rise directly with how much your teams use them. As adoption spreads, bills climb in ways that are difficult to anticipate in advance. The second cause is experimentation. AI projects are still maturing, so teams frequently test different tools, models, and approaches before finding what works. This trial-and-error phase generates spending that does not map neatly to a predictable plan, and failed experiments still cost money. The third cause is model and vendor volatility. Providers regularly change their prices, release new versions, and adjust their terms, meaning last quarter's assumptions may not hold this quarter. You have limited control over these external shifts. To manage this, insist on spending caps and alerts so no single project runs away unchecked. Require teams to estimate expected usage before launching, and review actual costs against those estimates monthly. Consolidate vendors where possible to gain negotiating leverage and simpler oversight. Finally, treat a portion of the AI budget explicitly as experimental, accepting that some spending will not yield immediate returns.

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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