Anthropic - Tygart Media

Category: Anthropic

News, analysis, and profiles covering Anthropic the company and its team.

  • Claude Code vs Windsurf: 2026 AI Coding Tool Comparison

    Claude Code vs Windsurf: 2026 AI Coding Tool Comparison

    Last refreshed: May 15, 2026

    Model Accuracy Note — Updated May 2026

    Current flagship: Claude Opus 4.7 (claude-opus-4-7). Current models: Opus 4.7 · Sonnet 4.6 · Haiku 4.5. Claude Opus 4.7 (claude-opus-4-7) is the current flagship as of April 16, 2026. Where this article references Opus 4.6 or earlier models, those references are historical. See current model tracker →. See current model tracker →

    Claude AI · Fitted Claude

    Claude Code and Windsurf represent two different visions of AI-assisted development — one terminal-native and model-focused, the other IDE-native and workflow-focused. Both are serious tools for professional developers in 2026. This comparison covers what actually matters: coding quality, context management, workflow fit, and cost.

    What They Are

    Long paper tape measure unrolling across a desk beside a laptop, metaphor for context window length
    What they are.

    Claude Code is Anthropic’s terminal-native AI coding tool. You install it as an npm package, authenticate with your Claude account, and work directly in your shell. It uses Claude models exclusively and has a 1-million-token context window for large codebases. It’s designed for developers who think in the command line.

    Windsurf (formerly Codeium) is an AI-native IDE — a full development environment built around AI assistance. It includes a traditional code editor with AI deeply embedded throughout: autocomplete, multi-file editing, natural language commands, and a chat interface. It supports multiple models including Claude, GPT-4o, and its own models.

    Feature Comparison

    Three stacked layers: chat UI, tools, agent runtime
    Feature comparison.
    Feature Claude Code Windsurf
    Interface Terminal Full IDE (VS Code-based)
    Model Claude only Multi-model (Claude, GPT-4o, own models)
    Context window 1M tokens Varies by model
    Autocomplete No Yes (supercomplete)
    Multi-file editing Yes Yes (Cascade)
    Git integration Yes Yes
    Codebase indexing Yes (via context) Yes (semantic search)
    Natural language commands Yes Yes (Cascade)
    Price Max sub ($100+/mo) or API Free tier + $15/mo Pro

    Model Performance

    Claude Code’s underlying model — Opus 4.6 — scores 80.8% on SWE-bench Verified, one of the highest published scores for any model on real-world engineering tasks. Windsurf can access Claude models via its multi-model architecture, but its proprietary models score lower on the same benchmark.

    If raw model performance on complex tasks is the priority, Claude Code’s direct access to Claude Opus 4.7 gives it an edge.

    Developer Experience

    Claude Code has a steeper initial learning curve — there’s no GUI, and effective use requires understanding how to structure prompts for agentic coding sessions. Once mastered, many developers find the terminal interface faster and less distracting than a full IDE.

    Windsurf has a gentler onboarding curve. Developers already comfortable in VS Code will feel at home immediately. The autocomplete, Cascade multi-file editing, and inline AI chat create a lower-friction introduction to AI-assisted coding.

    Pricing Reality

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    Pricing reality.

    This is where Windsurf has a clear advantage for cost-conscious developers. Windsurf’s Pro plan runs $15/month with a generous free tier. Claude Code requires Claude Max at $100/month minimum, or API usage (which can be cheaper for low-volume use but expensive at scale).

    For developers just starting with AI coding tools, Windsurf’s entry point is meaningfully more accessible.

    Choose Claude Code If You…

    • Prefer terminal-native workflows and spend most of your time in the shell
    • Work with very large codebases that benefit from the 1M token context window
    • Need the highest possible model performance on complex engineering tasks
    • Are already on a Claude Max subscription

    Choose Windsurf If You…

    • Want an IDE experience with AI deeply integrated throughout
    • Are new to AI coding tools and want a gentle learning curve
    • Need persistent autocomplete alongside agentic coding capabilities
    • Want model flexibility or lower entry cost

    Frequently Asked Questions

    Is Claude Code better than Windsurf?

    For terminal-native developers prioritizing model performance: Claude Code has the edge. For IDE-native developers wanting lower cost and full-featured editor integration: Windsurf is the better fit.

    Can Windsurf use Claude models?

    Yes. Windsurf supports multiple models including Claude. You can access Claude’s capabilities within the Windsurf environment, though Claude Code provides more direct and optimized access to Claude’s full context window.

    How much does Claude Code cost?

    Claude Code requires Claude Max ($100/month) or API billing. Windsurf starts at $15/month Pro with a free tier.


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  • Claude vs Gemini: Which AI Should You Use in 2026?

    Claude vs Gemini: Which AI Should You Use in 2026?

    Last refreshed: May 15, 2026

    Model Accuracy Note — Updated May 2026

    Current flagship: Claude Opus 4.7 (claude-opus-4-7). Current models: Opus 4.7 · Sonnet 4.6 · Haiku 4.5. Claude Opus 4.7 (claude-opus-4-7) is the current flagship as of April 16, 2026. Where this article references Opus 4.6 or earlier models, those references are historical. See current model tracker →. See current model tracker →

    Claude AI · Fitted Claude

    Claude and Gemini are the two most capable non-OpenAI AI assistants in 2026, and they’ve converged on similar pricing while diverging significantly in strengths. This comparison is based on real task testing across ten categories — not marketing copy or benchmark cherry-picking.

    Quick Verdict by Task

    Three stacked layers: chat UI, tools, agent runtime
    Quick verdict by task.
    Task Category Winner Why
    Long document analysis Claude 200K context, better synthesis quality
    Coding and software dev Claude 80.8% SWE-bench vs Gemini’s lower scores
    Research and summarization Gemini Real-time web access by default
    Image generation Gemini Native Imagen integration
    Image understanding Tie Both excellent
    Long-form writing quality Claude Less generic, better argumentation
    Google Workspace integration Gemini Native Docs, Gmail, Sheets integration
    Multimodal (video, audio) Gemini Gemini 2.0 handles video natively
    Safety and reliability Claude Constitutional AI, fewer hallucinations
    Free tier value Gemini More generous free access to capable models
    Not sure which to use?

    We’ll help you pick the right stack — and set it up.

    Tygart Media evaluates your workflow and configures the right AI tools for your team. No guesswork, no wasted subscriptions.

    The Core Architectural Difference

    Diagram comparing a long context window bar with a shorter output limit bar
    The core architectural difference.

    Claude was built by an AI safety company as its primary product. Every design decision — training methodology, Constitutional AI, refusal behavior — reflects that mission. The result is an assistant that reasons carefully, acknowledges uncertainty, and produces high-quality text and code.

    Gemini was built by Google as part of its search and productivity ecosystem. It’s deeply integrated with Google services, has native real-time web access, handles video and audio inputs, and generates images natively. It reflects Google’s multimodal ambitions.

    Writing Quality Comparison

    We gave both models identical prompts across five writing types: blog post intro, executive email, technical explanation, creative story opening, and marketing headline variations.

    Claude consistently produced cleaner, more specific prose with fewer generic constructions. Gemini was competent but occasionally defaulted to more templated structures. For long-form professional writing, Claude has the edge. For short-form or format-constrained writing, the gap narrows significantly.

    Coding Comparison

    Claude Opus 4.6 scores 80.8% on SWE-bench Verified — the leading benchmark for real-world software engineering tasks. Gemini’s published scores on the same benchmark are lower. In practice: Claude produces fewer hallucinated APIs, better handles complex multi-file refactoring, and provides more accurate debugging analysis.

    For developers choosing a primary AI coding assistant, Claude is the stronger choice. Gemini is more than adequate for routine coding tasks.

    Pricing Comparison

    Plan Claude Gemini
    Free Limited Sonnet Gemini 1.5 Flash (more generous)
    Standard paid $20/mo (Pro) $20/mo (Advanced)
    Power tier $100-200/mo (Max) $20/mo (Google One AI Premium includes Workspace)

    Gemini’s free tier is more generous. At the $20/month level, they’re similarly priced — but Gemini Advanced includes Google One storage and Workspace AI features, which Claude doesn’t. For pure AI assistant use, the value comparison is roughly equal.

    Choose Claude If You…

    • Do serious coding or software development
    • Work with long documents, legal files, or research papers regularly
    • Need the highest quality long-form writing output
    • Value careful reasoning and epistemic honesty over speed
    • Don’t need image generation or deep Google Workspace integration

    Choose Gemini If You…

    • Live in Google Workspace (Gmail, Docs, Sheets, Drive)
    • Need real-time web access as a default capability
    • Work with video, audio, or multimodal content
    • Need image generation built in
    • Want more generous free tier access

    The Both Approach

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    The both approach.

    Many professionals run both: Claude for deep work (long documents, complex writing, coding), Gemini for Google Workspace integration and quick research. At $20/month each, running both costs $40/month total — reasonable for knowledge workers who use AI daily.

    Frequently Asked Questions

    Is Claude better than Gemini for coding?

    Yes. Claude Opus 4.6 leads Gemini on SWE-bench coding benchmarks and produces fewer hallucinated APIs and better multi-file reasoning in real-world use.

    Is Gemini better than Claude for Google Workspace?

    Yes. Gemini has native integration with Gmail, Google Docs, Sheets, and Drive. Claude requires copy-pasting content or MCP integrations to access Google Workspace data.

    Which is cheaper, Claude or Gemini?

    Both cost $20/month at the standard tier. Gemini’s free tier is more generous. Claude’s power tiers ($100-200/month) have no direct Gemini equivalent.


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  • Is Claude AI Worth It? A Cost-Benefit Analysis for 2026

    Is Claude AI Worth It? A Cost-Benefit Analysis for 2026

    Last refreshed: May 15, 2026

    Claude AI · Fitted Claude

    The question isn’t whether Claude AI is good — it’s whether it’s worth paying for, at which tier, for your specific situation. This cost-benefit analysis breaks down what you actually get at each price point, calculates real cost-per-task, and gives a clear recommendation by user type.

    What You’re Paying For

    Three stacked layers: chat UI, tools, agent runtime
    What you’re paying for.

    Before running the numbers, it’s worth being clear about what Claude’s pricing tiers actually buy you. It’s not primarily about unlocking features — most features are available at every paid tier. It’s about usage capacity: how many messages you can send, how complex those messages can be, and whether you get access to the most powerful models.

    PlanPriceModel AccessApprox Heavy Messages/DayClaude CodeProjects
    Free$0Sonnet (limited)5–10NoNo
    Pro$20/moSonnet + Opus~12 heavy / more lightNoYes
    Max 5x$100/moSonnet + Opus~60 heavyYesYes
    Max 20x$200/moSonnet + Opus~240 heavyYesYes

    Cost-Per-Task Analysis

    Let’s calculate what Claude actually costs per completed task at each tier, assuming a “task” is a substantive prompt — analyzing a document, drafting a piece of content, debugging a function, or researching a question.

    Claude Pro ($20/month): If you’re averaging 12 heavy tasks per day, that’s roughly 360 tasks per month. Cost per task: $0.055. About 5.5 cents per substantive AI-assisted task. For context, a VA hour runs $15–25. A freelance writer charges $50–200/hour. Claude Pro at 5.5 cents per task is extraordinarily cheap if those tasks displace professional time.

    Claude Max 5x ($100/month): At ~60 heavy tasks/day, that’s 1,800 tasks/month. Cost per task: $0.056. Nearly identical per-task cost to Pro, but with 5x the volume. This is the value tier for power users.

    Claude Max 20x ($200/month): At ~240 heavy tasks/day, that’s 7,200 tasks/month. Cost per task: $0.028. The most cost-efficient tier per task if you’re actually using that volume.

    ROI by User Type

    Seven cards naming common AI chatbot failure modes
    ROI by user type.

    Freelance Writers and Content Creators

    If Claude saves you 2 hours of writing per week at a $75/hour effective rate, that’s $150/week or $600/month in recovered time. Claude Pro at $20/month pays for itself if it saves you 16 minutes per week. Verdict: Clear yes at Pro.

    Developers

    Claude Code is only available at Max 5x ($100/month) or via API. If Claude helps you resolve bugs, write tests, or understand a codebase faster — saving even 30 minutes of developer time per week at $100+/hour — the Max subscription pays for itself in a single day. Verdict: Max 5x is the right tier, and it’s cheap relative to dev billing rates.

    Researchers and Analysts

    The 200K context window for document analysis is the value driver. If you regularly read and synthesize long reports, contracts, or research papers, Claude Pro’s Projects feature (which maintains context across sessions) is a genuine workflow upgrade. Verdict: Pro is likely sufficient; upgrade to Max if you’re processing documents daily.

    Casual Users

    If you use AI for occasional questions, quick edits, or curiosity, the free tier is genuinely usable. The rate limits only frustrate sustained professional use. Verdict: Start free. Upgrade when you hit limits consistently.

    Small Business Owners

    Marketing copy, client emails, policy documents, job descriptions, SOPs — Claude Pro handles all of this. If it saves you 3 hours per month at your effective hourly rate, it’s paid for. Verdict: Pro is almost certainly worth it.

    When the Free Tier Is Enough

    • You need AI help a few times per week, not daily
    • Your tasks are typically short — quick edits, brief questions, simple summaries
    • You’re evaluating whether Claude fits your workflow before committing
    • You have another primary AI tool and want Claude as a secondary option

    When to Upgrade and Which Tier

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    When to upgrade and which tier.
    • Hit rate limits on free → Go Pro ($20)
    • Hit rate limits on Pro regularly → Go Max 5x ($100)
    • Need Claude Code → Max 5x minimum
    • Using Claude 8+ hours daily → Max 20x ($200)

    Related on Tygart Media: Claude pricing · Pro vs Max · how to use Claude.

    Frequently Asked Questions

    Is Claude AI free?

    Yes, Claude has a free tier with limited daily usage. Paid plans start at $20/month (Pro).

    Is Claude worth it compared to ChatGPT?

    At similar price points ($20/month), Claude and ChatGPT Plus are competitive. Claude generally wins on long documents and coding; ChatGPT wins on image generation and plugin ecosystem. Many professionals pay for both.

    What does Claude Max include?

    Claude Max ($100 or $200/month) includes higher usage limits, Claude Code access, extended thinking, and priority access during peak times.

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  • Claude AI Review 2026: Honest Assessment After 6 Months

    Claude AI Review 2026: Honest Assessment After 6 Months

    Last refreshed: May 15, 2026

    Model Accuracy Note — Updated May 2026

    Current flagship: Claude Opus 4.7 (claude-opus-4-7). Current models: Opus 4.7 · Sonnet 4.6 · Haiku 4.5. Claude Opus 4.7 (claude-opus-4-7) is the current flagship as of April 16, 2026. Where this article references Opus 4.6 or earlier models, those references are historical. See current model tracker →. See current model tracker →

    Claude AI · Fitted Claude

    Claude AI has become one of the most capable AI assistants available in 2026 — but it’s not perfect, and the official messaging undersells both its strengths and its real limitations. This review is based on sustained daily use across writing, coding, research, and analysis tasks. No affiliate relationship with Anthropic. Just what actually works and what doesn’t.

    What Claude Does Better Than Almost Anything Else

    Three stacked layers: chat UI, tools, agent runtime
    What Claude does better than almost anything else.

    Long-document analysis. Claude’s 200,000-token context window — roughly 150,000 words — is transformative for anyone who works with lengthy documents. Feed it an entire contract, research paper, financial report, or codebase and ask specific questions. The quality of synthesis is consistently better than competitors on complex, multi-page materials.

    Writing quality. Claude’s prose is the least robotic of any major AI model. It avoids the generic constructions (“In today’s fast-paced world…”) that mark AI output as AI output. With proper context, it can match sophisticated writing styles and produce genuinely useful drafts that require minimal editing.

    Coding. Opus 4.6 scores 80.8% on SWE-bench and 91.3% on GPQA Diamond — among the highest published scores of any model available. In practice, this translates to fewer hallucinated function names, better error diagnosis, and stronger multi-file reasoning than most alternatives.

    Honesty about uncertainty. Claude is more likely than competitors to say “I’m not sure” or “this is my best guess” rather than confidently stating something incorrect. For research and analysis tasks, this matters enormously.

    Real Benchmark Results

    Diagram comparing a long context window bar with a shorter output limit bar
    Real benchmark results.
    Benchmark Claude Opus 4.7 What It Measures
    SWE-bench Verified 80.8% Real-world GitHub issue resolution
    GPQA Diamond 91.3% PhD-level science reasoning
    HumanEval Top tier Code generation correctness
    MMLU Top tier Broad knowledge and reasoning

    Honest Cost Breakdown

    Plan Price Best For Real Daily Usage
    Free $0 Occasional use ~5-10 messages before throttling
    Pro $20/mo Regular professionals ~12 heavy prompts before rate limits
    Max 5x $100/mo Power users, devs ~60 heavy prompts/day
    Max 20x $200/mo Heavy daily use ~240 heavy prompts/day

    The Rate Limit Problem (The Real Frustration)

    This is the #1 complaint in every Claude user community and it’s legitimate. The Pro plan at $20/month throttles after roughly 12 “heavy” prompts — meaning prompts that require real computation, like complex analysis, long document reading, or code generation. You’ll hit the wall mid-session at the worst possible time.

    A viral Reddit post about this received 1,060+ upvotes. The community consensus: the Pro plan is underspecced for its price point, and jumping to Max 5x ($100/month) is a significant price jump for something that should be a smooth tier progression.

    Workarounds that help: using Projects with system prompts (reduces token overhead per conversation), preferring Sonnet over Opus for routine tasks (cheaper against limits), and batching related work into single longer sessions rather than many short ones.

    What Claude Can’t Do

    • Generate images: Claude cannot create images. Midjourney, DALL-E, or Adobe Firefly for that.
    • Real-time web access: No live browsing by default on the consumer interface. Knowledge has a training cutoff.
    • Remember between sessions by default: Memory exists but requires setup. Fresh sessions start fresh.
    • Replace specialized tools: Claude is general-purpose. For SEO research, use dedicated tools. For legal filing, use legal software. Claude augments specialists — it doesn’t replace them.

    Who Claude Is Worth It For

    Strong yes: Writers, researchers, developers, lawyers, consultants, analysts, product managers, HR professionals — anyone whose work involves reading, reasoning, writing, or coding at length.

    Consider alternatives: Users who primarily need image generation (ChatGPT/Midjourney), users who need deep Google Workspace integration (Gemini), or users running on a tight budget who won’t benefit from the Pro tier’s additional capacity.

    Start free, upgrade when you hit limits. The free tier is genuinely usable for orientation. When you find yourself frustrated by rate limits — which you will, if Claude is useful to you — that’s the signal to upgrade to Pro. If you hit Pro limits regularly, Max 5x is worth the jump.

    Final Verdict

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    Final verdict.

    Claude is one of the two or three best general-purpose AI assistants available in 2026. Its writing quality, document reasoning, and coding performance are among the strongest in the field. The rate limiting on lower tiers is a genuine frustration that Anthropic should address. The pricing jump from Pro to Max is steep. But for the right user — anyone doing serious knowledge work — Claude at the Max tier is worth it. Claude Pro at $20/month is competitive with ChatGPT Plus but hits limits faster for heavy use.

    Frequently Asked Questions

    Is Claude AI better than ChatGPT in 2026?

    For long-document analysis, coding, and nuanced writing: Claude holds a measurable advantage. For image generation, plugin ecosystem breadth, and Google Workspace integration: ChatGPT/Gemini are stronger. Most serious users use both.

    Is Claude Pro worth $20 a month?

    For regular professional use: yes, but with the caveat that the rate limits on Pro are tighter than they should be at this price point. Heavy users will want Max 5x ($100/month) within weeks.

    Does Claude have a free plan?

    Yes. The free tier gives limited daily access to Claude Sonnet 4.6. It’s useful for orientation but will frustrate anyone using Claude as a primary work tool.


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  • Claude Function Calling: Developer’s Guide to Tool Use

    Claude Function Calling: Developer’s Guide to Tool Use

    Last refreshed: May 15, 2026

    Claude AI · Fitted Claude

    Claude tool use (also called function calling) is the capability that transforms Claude from a conversational AI into an agentic system that can interact with external services, execute code, query databases, and take real-world actions. This guide covers how tool use works, the three execution modes, the built-in server tools, and practical implementation examples.

    What Is Tool Use?

    Flow from app/IDE through MCP to servers and data APIs
    What tool use is.

    Tool use lets you define functions that Claude can call during a conversation. When Claude determines that a tool would help answer a user’s request, it generates a tool call (specifying the tool name and arguments), your code executes the function, and the result is returned to Claude to continue the conversation.

    Example flow: User asks “What’s the weather in Seattle?” → Claude calls your get_weather function with {"location": "Seattle"} → Your code calls a weather API → Returns data to Claude → Claude generates a natural language response incorporating the weather data.

    Defining Tools

    Side-by-side when to use a script versus an agent
    Defining tools.
    tools = [
        {
            "name": "get_stock_price",
            "description": "Get the current stock price for a given ticker symbol",
            "input_schema": {
                "type": "object",
                "properties": {
                    "ticker": {
                        "type": "string",
                        "description": "The stock ticker symbol (e.g., AAPL, GOOGL)"
                    }
                },
                "required": ["ticker"]
            }
        }
    ]
    
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=tools,
        messages=[{"role": "user", "content": "What's Apple's current stock price?"}]
    )

    The Three Execution Modes

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    The three execution modes.

    1. Client-Side Execution

    Your application receives the tool call, executes the function locally or via external APIs, and returns the result. This is the standard pattern — you control the execution environment and can call any service.

    2. Server-Side Execution (Built-in Tools)

    Anthropic provides built-in tools that Claude can execute server-side without your code doing anything:

    • web_search: Real-time web search
    • code_execution: Execute Python code in a sandbox
    • bash: Run shell commands
    • text_editor: Read and edit files (used in Claude Code)

    3. Tool Runner SDK (Programmatic)

    Anthropic’s Tool Runner SDK automates the tool call/execute/return loop, letting you build agentic workflows without writing the orchestration loop manually.

    Handling Tool Results

    # After receiving a tool_use block from Claude
    if response.stop_reason == "tool_use":
        tool_use = next(block for block in response.content if block.type == "tool_use")
        tool_name = tool_use.name
        tool_input = tool_use.input
        
        # Execute your function
        result = your_function(tool_input)
        
        # Return result to Claude
        follow_up = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            tools=tools,
            messages=[
                {"role": "user", "content": "What's Apple's stock price?"},
                {"role": "assistant", "content": response.content},
                {"role": "user", "content": [{"type": "tool_result", "tool_use_id": tool_use.id, "content": str(result)}]}
            ]
        )

    Frequently Asked Questions

    What is the difference between tool use and function calling?

    They’re the same thing — Anthropic uses “tool use” as the preferred term, while “function calling” is the term OpenAI popularized. Both describe the same capability: letting an AI model invoke defined functions during a conversation.

    How many tools can I define for Claude?

    Claude supports up to several hundred tools in a single request, though performance is best with a focused set relevant to the task. Each tool definition consumes input tokens, so large tool sets have a cost impact.

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  • Claude Computer Use: Complete Setup Guide & Use Cases

    Claude Computer Use: Complete Setup Guide & Use Cases

    Last refreshed: May 15, 2026

    Claude AI · Fitted Claude

    Claude computer use is a capability that lets Claude control a computer — click buttons, type text, navigate browsers, run applications, and execute multi-step tasks as if it were a human operator. As of 2026, it’s one of the most powerful and underexplored capabilities in the Claude ecosystem. This tutorial covers what it is, how to set it up, what it’s actually useful for, and where it still falls short.

    What Is Claude Computer Use?

    Three stacked layers: chat UI, tools, agent runtime
    What Claude Computer Use is.

    Computer use is an API capability (not available in the standard Claude.ai interface) that lets Claude interact with a desktop environment via screenshots and tool calls. Claude sees the screen, decides what to click or type, executes that action, sees the updated screen, and continues — iterating until the task is complete.

    This is different from a browser extension or web scraper. Claude is operating a real (or virtualized) computer environment the same way a human would — by looking at the screen and interacting with what it sees.

    Current Benchmark Performance

    On OSWorld — the leading benchmark for computer use agents — Claude currently scores around 22% task completion on the most complex tasks. ChatGPT’s computer use scores higher on this specific benchmark at approximately 75%. This gap is real and matters for production use cases requiring high reliability. For simpler, more structured tasks, Claude’s computer use performs considerably better.

    Setting Up Claude Computer Use

    Side-by-side when to use a script versus an agent
    Setting up Claude Computer Use.

    Computer use requires API access. The basic setup:

    • Anthropic API key (API tier with computer use enabled)
    • A virtual machine or containerized desktop environment (Docker with a lightweight Linux desktop is the standard approach)
    • The Anthropic Python or TypeScript SDK

    Anthropic provides a reference implementation with a Docker-based Ubuntu environment, a noVNC interface for monitoring, and starter code. This is the fastest path to a working computer use setup.

    Best Current Use Cases

    • Web research and data extraction: Navigate websites, extract structured data, fill in forms — tasks that don’t have APIs
    • Software testing: Navigate UI flows, test edge cases, verify visual behavior
    • Repetitive desktop workflows: Tasks that require clicking through multiple application screens
    • Legacy software interaction: Applications without APIs where the only interface is visual

    Key Limitations to Know

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    Key limitations to know.
    • Reliability: Computer use is significantly less reliable than direct API calls for the same tasks. Where an API returns structured data, computer use can misread a screen or click the wrong element
    • Speed: Screenshot-based interaction is slow compared to direct integration
    • Cost: Each screenshot and tool call consumes API tokens; complex tasks can be expensive
    • Sensitive actions: Never use computer use for high-stakes irreversible actions (sending emails, making purchases) without human-in-the-loop verification

    Frequently Asked Questions

    Is Claude computer use available in Claude.ai?

    No. Computer use is an API capability available through the Anthropic API, not the standard Claude.ai web interface.

    How does Claude computer use compare to ChatGPT’s?

    On OSWorld benchmarks, ChatGPT’s computer use currently leads at approximately 75% vs Claude’s ~22%. For production use cases requiring high reliability, this gap matters. Both are improving rapidly.

    Need this set up for your team? Talk to Will →
  • Jared Kaplan: The Physicist Who Discovered AI Scaling Laws

    Jared Kaplan: The Physicist Who Discovered AI Scaling Laws

    Updated September 30, 2026.

    Claude AI · Fitted Claude

    Jared Kaplan is Anthropic’s co-founder and Chief Science Officer and the physicist who co-authored OpenAI’s 2020 scaling-laws paper, which showed language-model performance improves predictably with parameters, data, and compute. He also co-authored the GPT-3 paper, left OpenAI in 2021 with six colleagues to start Anthropic, and now leads science plus Responsible Scaling Policy release calls (including Claude 4). Forbes estimated his net worth at about $15.5 billion after Anthropic’s May 2026 funding round.

    Entity bios like this get compressed into AI answers; clean dates and citation-rate measurement matter as much as the headline.

    Academic background

    Physics first, then empirical laws for ML systems.

    Kaplan is a theoretical physicist with a Stanford BS and a Harvard PhD (2009, thesis on holography). He is an associate professor at Johns Hopkins University, on leave while at Anthropic. The through-line is compact laws for messy systems—quantum gravity first, language models second.

    The discovery that changed AI: scaling laws

    Compute, data, and model size tied to predictable loss improvements.

    In January 2020 Kaplan and colleagues at OpenAI published “Scaling Laws for Neural Language Models”. Cross-entropy loss improves as a power law when parameters, dataset size, and training compute grow—often across many orders of magnitude. Labs could estimate returns before a full cluster run.

    The paper underwrote the compute-heavy playbook behind GPT-4, Claude, and later frontier stacks. Later work added constraints (data quality, alignment cost), but the core bet—scale buys capability on a curve—still organizes capital in 2026.

    OpenAI years and GPT-3

    Kaplan joined OpenAI in 2019. He co-authored “Language Models are Few-Shot Learners” (2020)—the GPT-3 paper—and worked on early Codex-related research alongside Sam McCandlish, Tom Brown, Dario Amodei, and others who would co-found Anthropic.

    Co-founding Anthropic

    Seven OpenAI researchers founded Anthropic in 2021.

    Kaplan was one of seven OpenAI researchers who left in 2021 to found Anthropic. As Chief Science Officer he shapes Claude’s research direction. In October 2024 Anthropic named him responsible scaling officer for safety assessments under the Responsible Scaling Policy—work TIME cited when it put him on the TIME100 AI list for 2025 after his Claude 4 safety call.

    Washington and public policy

    CEO Dario Amodei has testified orally before Senate committees; Kaplan’s on-record policy work includes a December 2023 written statement to a Senate AI Insight Forum on risk and alignment, where he walked through scaling trends and Anthropic’s safety framing.

    Track how those sources surface in answers with an AI citation monitoring guide and LLM visibility measurement if you publish expert bios competitors scrape.

    Net worth and Forbes 400

    Forbes estimated Kaplan at roughly $3.7 billion in 2025, below that year’s Forbes 400 cutoff. After Anthropic’s $65 billion May 2026 round at about a $965 billion valuation, Forbes put each co-founder at about $15.5 billion, with Kaplan on the 2026 Forbes 400 tied near No. 75. See Forbes’s profile for the live estimate; reported IPO timing into late 2026 would move the number again.

    Why this profile matters for AI search

    Answer engines compress entity pages with primary links (OpenAI, Senate PDFs, Forbes, TIME) into one-line bios. Stale net-worth lines and vague “testified before Congress” wording drop out of GEO case studies. For citation mining on search surfaces, see the Bing citation mining experiment.

    Related on Tygart Media: Dario Amodei · Anthropic IPO · how to use Claude.

    Frequently Asked Questions

    What is Jared Kaplan known for?

    Jared Kaplan is best known for co-authoring AI scaling laws—the mathematical relationships that predict how language-model performance improves with more parameters, data, and training compute. His 2020 OpenAI paper “Scaling Laws for Neural Language Models” is widely treated as foundational for frontier model planning.

    What is Jared Kaplan’s role at Anthropic?

    Kaplan is Anthropic’s co-founder and Chief Science Officer. He also oversees Anthropic’s Responsible Scaling Policy process as the company’s responsible scaling officer, deciding safety assessments before major model releases.

    What is Jared Kaplan’s net worth?

    Forbes estimated Kaplan’s net worth at about $15.5 billion after Anthropic’s May 2026 funding round, when he joined the Forbes 400. Estimates move with private-market valuations and can change quickly.

    Did Jared Kaplan work at OpenAI?

    Yes. Kaplan joined OpenAI in 2019 and co-authored “Scaling Laws for Neural Language Models” (2020) and “Language Models are Few-Shot Learners” (2020), the paper behind GPT-3. He left with six colleagues to co-found Anthropic in 2021.

    What are AI scaling laws?

    Scaling laws are empirical power-law relationships showing that language-model loss improves smoothly as you scale model size, dataset size, and compute. Kaplan’s team showed labs could forecast capability gains before spending full training budgets—a logic that still drives frontier training in 2026.

    What is Jared Kaplan’s academic background?

    Kaplan trained as a theoretical physicist. He earned a PhD in physics from Harvard University in 2009 and is an associate professor at Johns Hopkins University (on leave while at Anthropic).


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  • What Is Claude AI? The Complete Guide (2026)

    What Is Claude AI? The Complete Guide (2026)

    Last refreshed: May 15, 2026

    Model Accuracy Note — Updated May 2026

    Lineup currency (Sept 2026, verified): Sonnet 5 ($2/$10), Opus 5.5 ($4/$20), Haiku 4.5 ($1/$5), Fable 5.1 ($10/$50). Legacy (still listed): Opus 4.8 ($5/$25), Sonnet 4.6 ($3/$15). Prior note (superseded): Prior flagship claim: Claude Opus 4.7 (claude-opus-4-7). Prior models claim: Opus 4.7 · Sonnet 4.6 · Haiku 4.5. Claude Opus 4.7 (claude-opus-4-7) was claimed current as of April 16, 2026. Where this article references Opus 4.6 or earlier models, those references are historical. See current model tracker →. See current model tracker →

    Claude AI · Fitted Claude

    Claude AI is a family of large language models built by Anthropic, a San Francisco-based AI safety company. In 2026, Claude competes directly with ChatGPT, Gemini, and Grok — and in many professional use cases, it outperforms all of them. This guide covers what Claude is, how it works, what it costs, and how to start using it today.

    What Is Claude AI?

    Three stacked layers: chat UI, tools, agent runtime
    What is Claude AI?

    Claude is an AI assistant developed by Anthropic, a company founded in 2021 by former OpenAI researchers including Dario Amodei, Daniela Amodei, and five other co-founders. The name “Claude” is a nod to Claude Shannon, the father of information theory.

    Unlike some AI tools built primarily for speed or image generation, Claude was designed from the ground up with safety and helpfulness as co-equal priorities. Anthropic uses a technique called Constitutional AI — a method of training models to follow a set of principles rather than just optimize for user approval. The result is an assistant that tends to be more careful, more honest, and less likely to hallucinate than its competitors.

    As of April 2026, Claude is available through:

    • Claude.ai — the web and mobile interface (free and paid plans)
    • Claude desktop app — native Mac and Windows applications
    • Claude API — for developers building AI-powered applications
    • Claude Code — a terminal-native AI coding tool
    • Enterprise deployments — via Anthropic’s enterprise and team offerings

    Which Claude Models Exist in 2026?

    Diagram comparing a long context window bar with a shorter output limit bar
    Which Claude models exist in 2026?

    Anthropic currently offers three tiers of Claude models, each optimized for different use cases:

    Model Best For Context Window Notable Benchmark
    Claude Opus 4.7 Complex reasoning, research, coding 200K tokens 80.8% SWE-bench, 91.3% GPQA Diamond
    Claude Sonnet 4.6 Everyday tasks, balanced performance 200K tokens Best speed-to-intelligence ratio
    Claude Haiku 4.5 Fast, lightweight tasks 200K tokens Fastest response time

    All models support a 200,000-token context window by default — roughly 150,000 words, or an entire novel. Enterprise customers can access up to 500,000 tokens, and Claude Code extends to 1 million tokens for large codebase analysis.

    How Does Claude AI Work?

    Claude is a large language model (LLM) — a type of neural network trained on vast amounts of text data to predict and generate human-like responses. What distinguishes Claude from other LLMs is Anthropic’s emphasis on alignment and safety during training.

    Claude uses two key training innovations:

    • Constitutional AI (CAI): Instead of relying solely on human feedback to shape model behavior, Anthropic trains Claude to evaluate its own outputs against a set of written principles. This makes Claude more consistent in avoiding harmful outputs, even in edge cases human reviewers might not anticipate.
    • RLHF (Reinforcement Learning from Human Feedback): Human trainers rate Claude’s responses, and those ratings guide the model toward more helpful, accurate, and appropriate answers over time.

    The combination produces a model that tends to acknowledge uncertainty, push back on false premises, and decline harmful requests more gracefully than many competitors.

    What Can Claude AI Do?

    Claude’s capabilities in 2026 span well beyond simple chatting. Here’s what it handles well:

    Writing and Editing

    Claude excels at long-form content: blog posts, essays, reports, marketing copy, email sequences, legal documents, and fiction. Its writing is notably less robotic than many AI tools, partly because it’s trained to match tone and style from context clues.

    Coding and Software Development

    Claude Code — Anthropic’s terminal-native coding tool — has become one of the most popular AI coding environments among professional developers. It can write, debug, refactor, and explain code across virtually all major programming languages, and it understands large codebases through its million-token context window.

    Research and Analysis

    Claude reads and synthesizes PDFs, research papers, financial reports, and legal filings. With 200K tokens of context, it can process an entire book-length document and answer specific questions about it.

    Data Analysis

    Claude can read CSV files, interpret charts, write Python or SQL to analyze datasets, and explain findings in plain language — making it useful for anyone who works with data but isn’t a dedicated data scientist.

    Multimodal Inputs

    Claude accepts text, images, PDFs, and documents as inputs. It can describe images, extract text from screenshots, and analyze visual data — though it cannot generate images itself (for image generation, tools like Midjourney or DALL-E are required).

    Claude AI Pricing: Free vs. Paid Plans in 2026

    Anthropic offers four main tiers for individual users:

    Plan Price What You Get Best For
    Free $0/month Limited daily messages, Claude Sonnet 4.6 access Casual or occasional use
    Claude Pro $20/month 5x more usage, priority access, Projects Regular users, professionals
    Claude Max 5x $100/month 5x Pro usage, Claude Code access, extended thinking Power users, developers
    Claude Max 20x $200/month 20x Pro usage, highest priority Heavy professional use

    Enterprise plans are available with custom pricing, SSO, admin controls, extended context (up to 500K tokens), and zero-data-retention options for sensitive industries.

    Claude vs. ChatGPT: What’s the Difference?

    This is the question most people ask when they first hear about Claude. The honest answer: they’re both capable, and the best choice depends on your use case.

    Factor Claude ChatGPT
    Best at Long documents, nuanced writing, coding General tasks, image generation, plugins
    Context window 200K tokens (standard) 128K tokens (GPT-4o)
    Image generation No (analysis only) Yes (DALL-E integration)
    Safety emphasis Very high (Constitutional AI) High
    Code quality Among the best (SWE-bench leader) Strong
    Price $20-$200/month $20/month (Plus), $200/month (Pro)

    For most professional writing, legal/financial analysis, and software development tasks, Claude holds a measurable edge. For tasks requiring image generation or deep integration with third-party plugins, ChatGPT’s ecosystem is broader.

    How to Get Started with Claude AI

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    How to get started with Claude AI.

    Getting started takes about two minutes:

    1. Go to claude.ai and create a free account with your email or Google login.
    2. Start a new conversation. Type or paste your first prompt.
    3. If you need to analyze a document, click the paperclip icon to upload PDFs, images, or files.
    4. For power use, upgrade to Claude Pro for Projects — a feature that lets you create persistent knowledge bases that Claude remembers across conversations.
    5. Spinning Up the API?

      I can walk you through setup, model selection, and cost management — before you burn credits figuring it out yourself.

      Email Will → will@tygartmedia.com

    6. If you’re a developer, visit console.anthropic.com to get your API key and explore the Claude API.

    Claude AI: Key Limitations to Know

    No tool is perfect. Here are Claude’s genuine limitations as of 2026:

    • No image generation: Claude cannot create images. For that, you need a dedicated tool like Midjourney, DALL-E, or Stable Diffusion.
    • Rate limits on free and Pro plans: Heavy users — especially on the Pro tier — regularly hit daily message limits. This is the most common complaint among power users. The Max plans ($100/$200/month) solve this for most use cases.
    • No real-time web access by default: Unless explicitly connected to a web search tool, Claude’s knowledge has a training cutoff. It cannot browse the web in real time by default on the consumer interface.
    • Occasional refusals: Claude’s safety training sometimes makes it overly cautious on topics that are legitimate but touch sensitive areas. This has improved substantially with each model generation.

    Frequently Asked Questions About Claude AI

    Is Claude AI free?

    Yes — Claude has a free tier that gives you limited daily access to Claude Sonnet 4.6. The free tier is useful for casual use, but heavy users will quickly encounter rate limits. Paid plans start at $20/month.

    Who made Claude AI?

    Claude was created by Anthropic, an AI safety company founded in 2021. Anthropic was started by seven former OpenAI researchers, including CEO Dario Amodei and President Daniela Amodei.

    Is Claude AI better than ChatGPT?

    It depends on the task. Claude generally outperforms ChatGPT on coding benchmarks, long-document analysis, and nuanced writing. ChatGPT has a broader plugin ecosystem and native image generation. Many professionals use both.

    Does Claude store my conversations?

    By default, Anthropic may use conversations from consumer accounts to improve its models (you can opt out in settings). Business and API customers can access zero-data-retention options. Conversation data is retained for up to five years unless you delete it manually.

    Can Claude generate images?

    No. Claude can analyze and describe images, but it cannot generate them. For AI image creation, use Midjourney, DALL-E, or Adobe Firefly.

    What is Claude’s context window?

    Standard Claude models have a 200,000-token context window — roughly 150,000 words. Enterprise plans extend this to 500,000 tokens. Claude Code supports up to 1 million tokens for large codebase analysis.

    How do I access Claude Code?

    Claude Code is available as part of the Claude Max subscription ($100+/month) or via the Anthropic API. It runs as a terminal-native tool — install it with npm install -g @anthropic-ai/claude-code and authenticate with your API key.


    This guide is updated regularly as Anthropic ships new models and features. Last updated: April 2026.


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  • Daniela Amodei: Co-Founder and President of Anthropic

    Daniela Amodei: Co-Founder and President of Anthropic

    Daniela Amodei is the President and co-founder of Anthropic, the AI safety company behind Claude. While her brother Dario Amodei serves as CEO and is the more publicly visible figure, Daniela runs the operational, commercial, and go-to-market sides of one of the most consequential AI companies in the world. She is, in practical terms, the reason Anthropic functions as a business.

    Quick facts: Daniela Amodei — President and co-founder of Anthropic. Previously VP of Operations at OpenAI. Before that: Stripe, Ropes & Gray. Co-founded Anthropic in 2021 with her brother Dario and five other former OpenAI researchers. Responsible for Anthropic’s business operations, sales, partnerships, and go-to-market strategy.

    Who Is Daniela Amodei?

    Three stacked layers: chat UI, tools, agent runtime
    Who is Daniela Amodei?

    Daniela Amodei is the President of Anthropic, the AI safety company she co-founded in 2021 alongside her brother Dario Amodei and a group of senior researchers who departed OpenAI together. While Dario leads research and product as CEO, Daniela leads everything that keeps the company running as a viable business: revenue, partnerships, hiring, operations, and the commercial strategy behind Claude.

    She is among the most powerful operators in the AI industry — not a figurehead co-founder, but the executive who built Anthropic’s commercial foundation from zero while the research team focused on the models.

    Background and Career Before Anthropic

    Before Anthropic, Daniela spent years in operational and business roles that would prove directly relevant to building a fast-moving AI company from scratch.

    She attended Dartmouth College, where she studied economics. Her early career included a position at Ropes & Gray, a prominent law firm, before moving into the technology sector. She joined Stripe — the payments infrastructure company — where she worked in business operations during a period of significant growth for the company.

    The pivotal move came when she joined OpenAI as VP of Operations. She was one of the senior leaders who left OpenAI in 2020 and 2021 along with her brother Dario to found Anthropic. That cohort included several of OpenAI’s most senior researchers and operators, making it one of the most significant team departures in AI industry history.

    Role at Anthropic

    Comparison of Claude how-to fit versus local service page fit for assistants
    Her role at Anthropic.

    As President, Daniela’s domain at Anthropic covers the business side of the company end to end. Where Dario focuses on research direction, safety philosophy, and model development, Daniela owns:

    • Revenue and commercial growth — enterprise sales, partnerships, and the Claude business
    • Go-to-market strategy — how Anthropic positions and sells Claude to individuals, developers, and enterprises
    • Operations — the internal systems and processes that let a growing AI company function
    • Partnerships — major deals including Anthropic’s relationship with Amazon Web Services, one of the largest infrastructure commitments in AI company history
    • Hiring and team building — scaling the organization while maintaining culture

    The division of labor between Daniela and Dario mirrors a pattern common in successful tech companies: one founder focused on product and technology, one focused on the business that makes the technology sustainable. At Anthropic, that structure is unusually clean and appears to function well.

    Daniela Amodei and the Amazon Partnership

    One of the most significant commercial milestones under Daniela’s leadership as President was securing Anthropic’s partnership with Amazon Web Services. Amazon committed to invest up to $4 billion in Anthropic, with Claude models made available through AWS’s Bedrock platform. This deal established Anthropic’s commercial credibility and gave it the infrastructure scale to compete with OpenAI and Google DeepMind.

    Partnerships of this scale require sustained executive relationships and months of commercial negotiation — the kind of work that falls squarely in Daniela’s domain.

    The Amodei Siblings Running Anthropic

    The dynamic between Daniela and Dario Amodei at Anthropic is worth understanding because it’s unusual. Co-founders who are siblings and who have distinct, non-overlapping domains are relatively rare. In most tech companies, co-founders compete for influence. At Anthropic, the operational split appears deliberate and functional: Dario owns the mission and the models, Daniela owns the machine that funds the mission.

    Dario has spoken publicly about AI safety, the risks of powerful AI systems, and Anthropic’s research philosophy. Daniela tends to operate more quietly — she is less frequently the face of Anthropic in press interviews but is consistently present in the company’s major commercial announcements and partnership moments.

    Net Worth and Anthropic’s Valuation

    Anthropic has raised billions of dollars in venture funding from investors including Google, Amazon, and Spark Capital, with valuations that have grown significantly through each funding round. As a co-founder and President holding equity in the company, Daniela Amodei’s net worth is tied primarily to Anthropic’s private valuation.

    Anthropic is not publicly traded, so precise figures are not available. At the company’s reported valuations, co-founders with meaningful equity stakes hold substantial paper wealth — though the actual liquidity of that wealth depends on if and when Anthropic conducts an IPO or secondary transactions.

    Why Daniela Amodei Matters for Claude

    Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key
    Why Daniela Amodei matters for Claude users and buyers.

    Claude exists because Anthropic exists as a viable company. Daniela Amodei is one of the primary reasons Anthropic is viable. The research team can build frontier AI models, but without a functioning commercial operation those models don’t reach users, don’t generate revenue, and don’t fund the next generation of research.

    Every enterprise Claude deployment, every API integration, every AWS customer using Claude through Bedrock, every API integration, every AWS customer using Claude through Bedrock — these exist in part because of the commercial infrastructure Daniela has built. The Claude you use is as much a product of her work as it is of the research team’s.

    Frequently Asked Questions

    Who is Daniela Amodei?

    Daniela Amodei is the President and co-founder of Anthropic, the AI company behind Claude. She previously served as VP of Operations at OpenAI before co-founding Anthropic in 2021 with her brother Dario Amodei and other former OpenAI researchers.

    Is Daniela Amodei related to Dario Amodei?

    Yes. Daniela and Dario Amodei are siblings. Dario is the CEO of Anthropic; Daniela is the President. They co-founded Anthropic together in 2021 along with five other former OpenAI researchers.

    What does Daniela Amodei do at Anthropic?

    As President, Daniela oversees Anthropic’s business operations, commercial strategy, revenue, partnerships, and go-to-market. She is responsible for the business side of Anthropic while Dario leads research and product.

    Where did Daniela Amodei work before Anthropic?

    Before co-founding Anthropic, Daniela was VP of Operations at OpenAI. Prior to OpenAI she worked at Stripe in business operations, and earlier in her career she was at the law firm Ropes & Gray. She studied economics at Dartmouth College.

    What is Daniela Amodei’s net worth?

    Daniela Amodei’s net worth is not publicly known — Anthropic is a private company and does not disclose individual equity stakes. Her net worth is tied primarily to her equity in Anthropic, which has been valued at billions of dollars across successive funding rounds from investors including Amazon and Google.




  • Claude Managed Agents — Complete Pricing Reference + Dreaming Update (May 2026)

    Claude Managed Agents — Complete Pricing Reference + Dreaming Update (May 2026)

    Last refreshed: May 15, 2026

    May 2026 Update — Dreaming Feature + Beta Status

    Anthropic introduced Dreaming at Code w/ Claude (May 6, 2026) — a new Managed Agents capability where agents review their own session history overnight to improve future performance. Harvey (legal AI) reported a roughly 6× task completion rate increase after implementing it. Dreaming is developer-access preview only. Multiagent Orchestration and Outcomes are now in public beta. See the new Dreaming section below.

    What Is Claude Managed Agents? (Current Status, May 2026)

    Four-step loop: observe, remember, act, update for managed agents
    What Claude Managed Agents is right now.

    Claude Managed Agents is Anthropic’s framework for long-running, stateful AI agents — agents that can maintain context across sessions, hand off between sub-agents, and now, improve themselves by reviewing their own work history. Here’s the current status of each component:

    Component Status Who Has Access
    Multiagent Orchestration Public Beta All API developers
    Outcomes Public Beta All API developers
    Dreaming Developer Preview Selected developers only

    Dreaming: The Feature the Press Mostly Missed

    Three stacked layers: chat UI, tools, agent runtime
    Dreaming — the feature the press mostly missed.

    Announced at Code w/ Claude on May 6, 2026, Dreaming is a Managed Agents capability that lets agents review and reorganize their own memory between sessions. The mechanism:

    1. After a session ends, the agent reads its existing memory store alongside the session transcripts
    2. It produces a new, reorganized memory store: duplicates merged, stale entries replaced, new patterns surfaced
    3. The next session starts with a higher-quality knowledge base — capturing insights no single session could hold

    This is meaningfully different from simply persisting conversation history. The agent isn’t just remembering what happened — it’s synthesizing what it learned. Think of it as the difference between taking notes and actually reviewing and reorganizing your notes the next morning.

    The Harvey Result

    Harvey, the legal AI company, reported approximately a 6× task completion rate increase after implementing Dreaming in their Managed Agents workflow. Harvey’s use case — complex legal research that spans multiple sessions with evolving context — is exactly the kind of work Dreaming was designed for. Sessions build on each other rather than starting fresh each time.

    Dreaming is developer-access preview as of May 2026. Docs: platform.claude.com/docs/en/managed-agents/dreams.

    What Dreaming Is Not

    A few clarifications worth making explicit:

    • Dreaming is not available to end users — it’s a developer-layer capability requiring implementation
    • It’s not persistent memory in the claude.ai chat interface
    • It’s not available to free or standard Pro subscribers through any interface
    • It’s a developer preview, not GA — expect it to evolve before full release

    Our Take: Why This Architecture Matters

    We run Managed Agents in our own Cowork workflows. The Dreaming announcement is the first time Anthropic has shipped something that resembles how expert human knowledge actually compounds over time — not by accumulating raw notes, but by periodically synthesizing and reorganizing what’s been learned into a cleaner structure.

    The Harvey 6× result is a real-world data point from a production legal AI workflow. That’s not a benchmark number — it’s a deployed system showing measurable improvement from session-to-session memory refinement. Whether that 6× figure holds across different use cases is unknown, but the direction of the effect is the signal: agents that learn from their own history outperform agents that don’t.

    For non-developer users watching this space: Dreaming is the preview of what agentic AI will look like when it becomes mainstream. The groundwork being laid now in developer preview will eventually surface in subscription-tier products.

    Model Accuracy Note — Updated May 2026

    Lineup currency (Sept 2026, verified): Sonnet 5 ($2/$10), Opus 5.5 ($4/$20), Haiku 4.5 ($1/$5), Fable 5.1 ($10/$50). Legacy (still listed): Opus 4.8 ($5/$25), Sonnet 4.6 ($3/$15). Prior note (superseded): Prior flagship claim: Claude Opus 4.7 (claude-opus-4-7). Prior models claim: Opus 4.7 · Sonnet 4.6 · Haiku 4.5. Claude Opus 4.7 (claude-opus-4-7) was claimed current as of April 16, 2026. Where this article references Opus 4.6 or earlier models, those references are historical. See current model tracker →. See current model tracker →

    Tygart Media Strategy
    Volume Ⅰ · Issue 04Quarterly Position
    By Will Tygart
    • Long-form Position
    • Practitioner-grade

    You opened this tab because you need a number you can actually use. Not a vibe, not “it depends.” A real pricing breakdown you can put in a spreadsheet, a budget request, or a Slack message to your CTO.

    This is that page. Every pricing variable for Claude Managed Agents in one place, verified against Anthropic’s current documentation as of April 2026. Bookmark it. The beta will update; so will this.

    Quick Reference: The Formula

    Total Cost = Token Costs + Session Runtime ($0.08/hr) + Optional Tools
    Session runtime only accrues while status = running. Idle time is free.

    The Two Cost Dimensions

    Workshop fuel gauge and metal tokens pouring into an API hopper, metaphor for pay-per-token pricing
    Two cost dimensions — stale-proof framing.

    Claude Managed Agents bills on exactly two dimensions: tokens and session runtime. Every pricing question you have collapses into one of these two buckets.

    Dimension 1: Token Costs

    These are identical to standard Claude API pricing. You pay the same rates you’d pay calling the Messages API directly. No Managed Agents markup on tokens. Current rates for the models most commonly used in agent work:

    • Claude Sonnet 4.6 (legacy — still listed): ~$3/million input tokens, ~$15/million output tokens
    • Claude Opus 4.7: higher rates apply — check platform.claude.com/docs/en/about-claude/pricing for current figures
    • Prompt caching: same multipliers as standard API — cache hits dramatically reduce input token costs on long sessions with stable system prompts

    The implication: a token-heavy agent with a large system prompt that runs the same context repeatedly benefits significantly from prompt caching, and that benefit carries over unchanged into Managed Agents.

    Dimension 2: Session Runtime — $0.08/Session-Hour

    This is the Managed Agents-specific charge. You pay $0.08 per hour of active session runtime, metered to the millisecond.

    The critical word is active. Runtime only accrues while your session’s status is running. The following do not count toward your bill:

    • Time spent waiting for your next message
    • Time waiting for a tool confirmation
    • Idle time between tasks
    • Rescheduling delays
    • Terminated session time

    This is not how you’d bill a virtual machine. It’s closer to how AWS Lambda bills — you pay for execution, not reservation. An agent that “runs” for 8 hours but spends 6 of those hours waiting on human input has a very different bill than one running continuous autonomous loops.

    Optional Tool Costs

    Web Search: $10 per 1,000 Searches

    If your agent uses web search, each search costs $10/1,000 — that’s $0.01 per search. For most agents, this is negligible. For a research agent running hundreds of searches per session, it becomes a line item worth modeling separately.

    Code Execution: Included in Session Runtime

    Code execution containers are included in your $0.08/session-hour charge. You’re not separately billed for container hours on top of session runtime. This is explicitly stated in Anthropic’s docs and represents meaningful savings versus provisioning your own compute.

    Worked Cost Examples

    Example 1: Daily Research Agent

    Runs once per day. 30 minutes of active execution. Processes 10 documents, outputs a summary report. Moderate token volume.

    • Session runtime: 0.5 hrs × $0.08 = $0.04/day (~$1.20/month)
    • Tokens (estimate): 50K input + 5K output with Sonnet 4.6 = ~$0.23/run (~$7/month)
    • Total: ~$8–10/month

    Example 2: Weekly Batch Content Pipeline

    Runs 3x/week. 2-hour active sessions. Processes multiple documents, generates structured outputs.

    • Session runtime: 2 hrs × $0.08 × 12 sessions/month = $1.92/month
    • Tokens: depends on content volume — typically $10–40/month
    • Total: ~$12–42/month

    Example 3: Customer Support Agent (Business Hours)

    Active during business hours, handling tickets. 8 hours/day active, 5 days/week.

    • Session runtime: 8 hrs × $0.08 × 22 days = $14.08/month in runtime
    • Tokens: highly variable by ticket volume — the dominant cost driver at scale
    • Runtime cost alone: ~$14/month — tokens are likely 5–20x this depending on volume

    Example 4: 24/7 Always-On Agent

    The maximum theoretical runtime exposure. Continuous operation, no idle time.

    • Session runtime: 24 hrs × $0.08 × 30 days = $57.60/month
    • In practice, no agent has zero idle time — real cost will be lower
    • Token costs at this scale become the dominant factor by a wide margin

    Anthropic’s Official Example (from their docs)

    A one-hour coding session using Claude Opus 4.7 consuming 50,000 input tokens and 15,000 output tokens: session runtime = $0.08. With prompt caching active and 40,000 of those tokens as cache reads, the token costs drop significantly. The runtime charge stays flat at $0.08 regardless of caching.

    What’s Not Billed in Managed Agents

    A few things that might seem like costs but aren’t:

    • Infrastructure provisioning: Anthropic handles hosting, scaling, and monitoring at no additional charge
    • Container hours: Explicitly not separately billed on top of session runtime
    • State management and checkpointing: Included in the session runtime charge
    • Error recovery and retry logic: Anthropic’s infrastructure problem, not yours

    Rate Limits

    Managed Agents has specific rate limits separate from standard API limits:

    • Create endpoints: 60 requests/minute
    • Read endpoints: 600 requests/minute
    • Organization-level limits still apply
    • For higher limits, contact Anthropic enterprise sales

    How to Access Managed Agents Pricing

    Managed Agents is available to all Anthropic API accounts in public beta. No separate signup, no premium tier gate. You need the managed-agents-2026-04-01 beta header in your API requests — the Claude SDK adds this automatically.

    For high-volume agent applications, Anthropic’s enterprise sales team negotiates custom pricing arrangements. Contact them at [email protected] or through the Claude Console.

    The Pricing Signals Worth Noting

    Anthropic recently ended Claude subscription access (Pro/Max) for third-party agent frameworks, requiring those users to switch to pay-as-you-go API pricing. This signals a deliberate strategy: consumer subscriptions are for human-paced interactions; agent workloads route through the API. The $0.08/session-hour rate exists in that context — it’s infrastructure pricing for compute that runs beyond human attention spans.

    The session-hour model also signals something about Anthropic’s infrastructure cost structure. They’re pricing on active execution time because that’s what actually taxes their systems. Idle sessions don’t cost them much; active agents do. The billing model follows the actual resource consumption pattern.

    Frequently Asked Questions

    Is the $0.08/session-hour charge in addition to token costs, or does it replace them?

    In addition to. You pay both: standard token rates for all input and output tokens, plus $0.08 per hour of active session runtime. They’re separate line items.

    Does prompt caching work in Managed Agents sessions?

    Yes. Prompt caching multipliers apply identically to Managed Agents sessions as they do to standard API calls. If your agent has a large, stable system prompt, caching it can significantly reduce input token costs.

    What happens if my session crashes? Am I billed for the crashed time?

    Runtime accrues only while status is running. Terminated sessions stop accruing. Anthropic’s infrastructure handles checkpointing and crash recovery — the session state is preserved even if the session terminates unexpectedly.

    Can I use Managed Agents on the free API tier?

    Managed Agents is available to all Anthropic API accounts in public beta, but standard tier access and rate limits apply. Free API tier users receive a small credit for testing.

    How does this compare to running agents on my own infrastructure?

    See our full breakdown: Build vs. Buy: The Real Infrastructure Cost of Claude Managed Agents. Short version: the $0.08/hour is almost certainly cheaper than provisioning and maintaining equivalent compute, but you trade control and data locality for that simplicity.

    Are there volume discounts?

    Volume discounts are available for high-volume users but negotiated case-by-case. Contact Anthropic enterprise sales.

    Does web search billing count against the $10/1,000 rate if the search returns no results?

    Anthropic’s current docs don’t explicitly address failed searches. Treat any triggered search as billable until confirmed otherwise.

    For the full session-hour math worked out by workload type, see: Claude Managed Agents Pricing, Decoded: What a Session-Hour Actually Costs You. For the build-vs-buy infrastructure comparison: Build vs. Buy: The Real Infrastructure Cost. For enterprise deployment patterns: Rakuten Stood Up 5 Enterprise Agents in a Week.