Tag: Claude

  • Claude Message Batches API: 50% Pricing, Limits and How It Works (2026)

    Claude Message Batches API: 50% Pricing, Limits and How It Works (2026)

    Last verified: June 13, 2026

    The Message Batches API lets you submit up to 100,000 Claude requests in a single call and receive results asynchronously — at exactly 50% of standard token prices. Most batches finish in under an hour. Results remain downloadable for 29 days. This page covers every verified limit, the per-tier rate limit tables, and how batch pricing stacks with prompt caching.

    Pricing: 50% off standard rates

    Every token processed through the Message Batches API is billed at half the standard input and output price. No quality difference from synchronous requests — only timing. The table below shows verified batch prices for active models.

    Model Batch input (per MTok) Batch output (per MTok) Standard input (per MTok) Standard output (per MTok)
    Claude Fable 5 $5.00 $25.00 $10.00 $50.00
    Claude Opus 4.8 $2.50 $12.50 $5.00 $25.00
    Claude Opus 4.7 $2.50 $12.50 $5.00 $25.00
    Claude Opus 4.6 $2.50 $12.50 $5.00 $25.00
    Claude Opus 4.5 $2.50 $12.50 $5.00 $25.00
    Claude Sonnet 4.6 $1.50 $7.50 $3.00 $15.00
    Claude Sonnet 4.5 $1.50 $7.50 $3.00 $15.00
    Claude Haiku 4.5 $0.50 $2.50 $1.00 $5.00

    Source: platform.claude.com/docs/en/build-with-claude/batch-processing

    Key limits at a glance

    Limit Value
    Maximum requests per batch 100,000
    Maximum batch payload size 256 MB
    Typical completion time Under 1 hour
    Hard expiration window 24 hours from creation
    Result retention period 29 days after creation
    Zero Data Retention eligible No
    Results format JSONL, streamed via results_url
    Supported models All active Claude models

    A batch expires if processing has not completed within 24 hours. Any individual request within that batch that did not finish is marked expired — you are not billed for expired or errored requests. Batch results (the JSONL file) are accessible for download for 29 days after the batch was created; after that the batch object itself is still visible but results can no longer be downloaded.

    Message Batches API rate limits by tier

    The Message Batches API has its own rate-limit pool, shared across all models, separate from the standard Messages API limits. The “processing queue” count refers to individual batch requests (not batches) that have been submitted but not yet completed by the model.

    Tier RPM (API calls) Max batch requests in processing queue Max batch requests per batch
    Tier 1 50 100,000 100,000
    Tier 2 1,000 200,000 100,000
    Tier 3 2,000 300,000 100,000
    Tier 4 4,000 500,000 100,000

    Source: platform.claude.com/docs/en/api/rate-limits

    RPM here limits how fast you can make HTTP requests to the Batches API endpoints (create, retrieve, list, cancel). It does not limit how many individual requests inside a batch are processed per minute — that is governed by the queue cap above. If high demand causes processing to slow, more individual requests within a batch may reach the 24-hour expiration limit.

    Stacking batch pricing with prompt caching

    The Batches API documentation explicitly states that the 50% batch discount and prompt caching discounts stack. Cache writes incur a one-time cost at 1.25x the base input rate (5-minute TTL) or 2x (1-hour TTL); subsequent cache reads cost 0.1x the base input rate. Because batches process asynchronously and may take longer than 5 minutes, Anthropic recommends using the 1-hour cache duration for batch requests that share large context.

    The following example uses Claude Opus 4.8 (standard input: $5.00/MTok) to show what each token type costs in a batch with a 1-hour cached system prompt.

    Token type Multiplier applied Effective price per MTok How calculated
    Uncached input (standard) 1x $5.00 Baseline
    Uncached input (batch) 0.5x $2.50 50% batch discount
    Cache write — 1h TTL (batch) 2x × 0.5x = 1x $5.00 2x write cost, then 50% batch
    Cache read (batch) 0.1x × 0.5x = 0.05x $0.25 10% read cost, then 50% batch
    Output (batch) 0.5x of $25.00 $12.50 50% batch discount on output

    In practice: if you cache a 50,000-token system prompt once and then read it across 1,000 batch requests, the cache write costs $0.25 (50K tokens at $5.00/MTok effective), while 1,000 cache reads cost $12.50 total (50M tokens at $0.25/MTok). The same 50 million tokens without caching would cost $125 in batch input (50 MTok at the $2.50/MTok batch rate). Cache hit rates on batches vary; Anthropic’s documentation notes typical rates of 30% to 98% depending on traffic patterns, since batch requests are processed concurrently rather than sequentially.

    How results come back

    When the batch finishes (or the 24-hour limit is reached), a results_url property is set on the batch object. Results are in JSONL format — one JSON object per line, in any order (not necessarily matching submission order). Each result carries the custom_id you assigned, plus a result object of type succeeded, errored, canceled, or expired. Streaming the results file rather than downloading it all at once is recommended for large batches. You are not billed for errored, canceled, or expired requests.

    Does the Batches API count against my standard Messages API rate limits?

    No. The Message Batches API has its own rate-limit pool that is tracked separately from the standard Messages API RPM, ITPM, and OTPM limits. You can use both simultaneously up to their respective limits.

    What happens if my batch does not finish within 24 hours?

    Any individual requests within the batch that did not complete are marked expired. You are not billed for those requests. The batch itself moves to ended status and whatever results did complete are available at the results_url.

    Can I use extended thinking, tool use, or vision in a batch?

    Yes. The Batches API supports vision, tool use (including server tools such as web search and code execution), system messages, multi-turn conversations, and extended thinking. The parameters not supported are stream: true, fast mode (speed), Threads parameters, and max_tokens: 0.

    How long are batch results available for download?

    Results are available for 29 days after the batch was created. After that window, the batch object remains visible in the Console and via the API, but the results file can no longer be downloaded.

    Is the Batches API eligible for Zero Data Retention?

    No. The Message Batches API is explicitly excluded from Zero Data Retention (ZDR). Data is retained under the feature’s standard retention policy regardless of your organization’s ZDR settings.

  • How Many Words Is a Million Claude Tokens? (2026) — and How the New Tokenizer Changed the Math

    How Many Words Is a Million Claude Tokens? (2026) — and How the New Tokenizer Changed the Math

    Last verified: June 13, 2026

    A million Claude tokens equals roughly 750,000 words on Claude Sonnet 4.6 — but only about 555,000 words on Claude Opus 4.7, Claude Opus 4.8, and Claude Fable 5. The gap comes from a new tokenizer that Anthropic introduced with Opus 4.7: it emits up to 35% more tokens from the same text. The only reliable way to measure your actual token count is the /v1/messages/count_tokens endpoint.

    Token-to-word conversion by model (1 million tokens)

    Anthropic publishes word equivalents directly in the context-window tooltips on the official models overview page. The figures below come from those tooltips.

    Model Tokenizer Context window ~Words per 1M tokens ~Pages per 1M tokens*
    Claude Fable 5 (claude-fable-5) New (Opus 4.7) 1M tokens ~555,000 ~2,200
    Claude Opus 4.8 (claude-opus-4-8) New (Opus 4.7) 1M tokens ~555,000 ~2,200
    Claude Opus 4.7 (claude-opus-4-7) New (Opus 4.7) 1M tokens ~555,000 ~2,200
    Claude Sonnet 4.6 (claude-sonnet-4-6) Older 1M tokens ~750,000 ~3,000
    Claude Haiku 4.5 (claude-haiku-4-5) Older 200k tokens ~150,000 (200K context) ~600 (200K context)
    Claude Opus 4.6 (claude-opus-4-6) Older 1M tokens ~750,000 ~3,000

    * Pages estimated at ~250 words per double-spaced page. These are approximations for typical English prose; actual counts vary by content type.

    What the new tokenizer changed — and why it matters

    Anthropic introduced a new tokenizer with Claude Opus 4.7. The official migration guide states that the new tokenizer “may use roughly 1x to 1.35x as many tokens when processing text compared to previous models (up to ~35% more, varying by content).” The most commonly cited figure across Anthropic’s documentation is roughly 30% more tokens for the same text.

    The practical effect: a document that costs 1,000,000 tokens on Opus 4.6 or Sonnet 4.6 costs approximately 1,300,000 tokens on Opus 4.7, Opus 4.8, or Fable 5. Budgets built for the old tokenizer need to be re-baselined against the new one.

    Tokenizer Models Approximate token increase vs. older tokenizer
    New (introduced Opus 4.7) Opus 4.7, Opus 4.8, Fable 5, Mythos 5 ~30% typical; up to ~35% depending on content
    Older Opus 4.6, Sonnet 4.6, Haiku 4.5, Opus 4.5, Sonnet 4.5 Baseline

    The token counting page also notes the comparison directly: “Claude Fable 5 and Claude Mythos 5 use the tokenizer introduced with Claude Opus 4.7, which produces roughly 30% more tokens than models before Claude Opus 4.7 for the same text.”

    Use count_tokens — not tiktoken or ratio math

    Anthropic’s migration guide explicitly flags the risk: “Any code path that estimates tokens client-side or assumes a fixed token-to-character ratio should be re-tested against Claude Opus 4.7.” OpenAI’s tiktoken library is trained on a different vocabulary and produces different counts. It will not give accurate results for any Claude model.

    The correct approach is the /v1/messages/count_tokens endpoint, passing the specific model you intend to use:

    curl https://api.anthropic.com/v1/messages/count_tokens \
      --header "x-api-key: $ANTHROPIC_API_KEY" \
      --header "content-type: application/json" \
      --header "anthropic-version: 2023-06-01" \
      --data '{
        "model": "claude-opus-4-8",
        "messages": [{"role": "user", "content": "Your text here"}]
      }'

    The endpoint returns a model-specific count. If you are migrating a workload from Sonnet 4.6 to Opus 4.8, count the same prompt with both model IDs and compare the two input_tokens values. The token counting endpoint is free to use (rate limits apply by usage tier). Anthropic notes that the returned count is an estimate; the actual count at inference time may differ by a small amount.

    Quick reference: common document sizes

    Document type Approx. words Tokens (older tokenizer) Tokens (new tokenizer)
    Novel (~400 pages) ~100,000 ~133,000 ~173,000
    Long research paper ~20,000 ~27,000 ~35,000
    Full context, Sonnet 4.6 (1M tokens) ~750,000 1,000,000 N/A (different model)
    Full context, Opus 4.8 (1M tokens) ~555,000 N/A (different model) 1,000,000

    These word estimates assume typical English prose. Code, structured data, and non-Latin scripts tokenize differently from natural language prose. Highly repetitive text and dense symbol-heavy content (like JSON or code) can fall well outside the ~0.75 words-per-token ratio.

    Does the new tokenizer change what fits in the context window?

    Yes, in one direction. The context window is still 1M tokens, but that window holds fewer words on the new tokenizer (~555k words) than on the old one (~750k words). A document that previously fit comfortably may now require trimming or chunking when moving to Opus 4.7, Opus 4.8, or Fable 5.

    Does Sonnet 4.6 use the new tokenizer?

    No. Claude Sonnet 4.6 uses the older tokenizer. Anthropic’s model overview page lists Sonnet 4.6’s 1M-token context window as equivalent to ~750k words, the same ratio as Opus 4.6 — confirming it has not adopted the Opus 4.7 tokenizer. Only Opus 4.7, Opus 4.8, Fable 5, and Mythos 5 use the new tokenizer.

    Can I use tiktoken or another open-source tokenizer for Claude?

    No. tiktoken is built for OpenAI models and uses a different vocabulary. It will not produce accurate token counts for any Claude model, and its error will be larger on the new Opus 4.7 tokenizer than on older Claude models. Use /v1/messages/count_tokens with the specific Claude model ID you plan to deploy.

    Does the new tokenizer affect pricing?

    Yes. Billing reflects token counts under the model’s tokenizer. If you migrate a workload from Opus 4.6 to Opus 4.8 and the new tokenizer produces 30% more tokens, your input token costs increase by roughly 30% before accounting for any per-token price difference between the models. Re-baseline cost estimates using the count_tokens endpoint rather than scaling from old measurements.

    How many pages is the full 1M-token context window?

    On models with the older tokenizer (Sonnet 4.6, Opus 4.6), 1 million tokens is approximately 3,000 double-spaced pages of typical English prose. On models with the new tokenizer (Opus 4.8, Fable 5), the same 1 million tokens holds approximately 2,200 pages. These are prose estimates — a 1M-token window filled with source code or dense structured data will span a very different page count.

  • Claude Cowork vs Code vs Agent SDK vs Managed Agents (2026)

    Claude Cowork vs Code vs Agent SDK vs Managed Agents (2026)

    Last verified: June 13, 2026

    Anthropic ships four distinct ways to put Claude to work as an agent, and they are easy to confuse. The short version: Claude Cowork and Claude Code are interactive products billed through your Claude subscription — Cowork for knowledge work in the desktop app, Code for software work in your terminal, IDE, desktop, or browser. The Claude Agent SDK and Managed Agents are programmatic surfaces for developers, billed through the API: the Agent SDK is a Python/TypeScript library that runs the agent loop inside your own process, while Managed Agents is a REST API where Anthropic runs the loop and hosts the sandbox. The tables below give the verified, side-by-side breakdown.

    The decision matrix

    Each row is one surface. Read across for who it serves, whether you drive it turn-by-turn or hand it a goal, where the work executes, and how it is paid for.

    Surface Who it is for Interactive vs autonomous Where it runs How it is billed
    Claude Cowork Knowledge workers (non-developers) — research, documents, file and spreadsheet work Interactive, supervised — shows you the plan and waits for your approval before acting The Claude desktop app on your own computer (macOS or Windows); not available on web or mobile Claude subscription (Pro, Max, Team, Enterprise) — draws from your plan’s usage allocation
    Claude Code Developers doing interactive coding — build features, fix bugs, automate dev tasks Interactive — you drive it in a session, though it can run agentically across files and tools Your machine (terminal, VS Code, JetBrains, desktop app) or the browser at claude.ai/code Claude subscription or an Anthropic Console (API) account
    Claude Agent SDK Developers building custom agents programmatically (Python or TypeScript) Autonomous — Claude reads files, runs commands, and edits code on its own via the agent loop Your own process and infrastructure API key (pay-as-you-go credits); see the subscription note below for the June 15, 2026 change
    Managed Agents Developers running production or long-running agents without operating their own sandbox/session infrastructure Autonomous — you send events, Claude executes tools and streams back results Anthropic-managed cloud sandbox per session (or a self-hosted sandbox on your own infrastructure) Claude API key + the managed-agents-2026-04-01 beta header (no subscription path)

    Where billing actually differs

    The cleanest way to split these four is by the wallet they draw from. The two interactive products are funded by a subscription; the two programmatic surfaces are funded by the API. This is the single distinction that trips people up most often, so it is worth stating plainly in its own table.

    Surface Billing model Notes
    Claude Cowork Subscription Included on Pro, Max, Team, and Enterprise. Multi-step tasks consume more of your usage allocation than chatting.
    Claude Code Subscription or API Most surfaces require a Claude subscription or a Console account; the terminal CLI and VS Code also support third-party providers.
    Claude Agent SDK API (pay-as-you-go) Authenticated with an ANTHROPIC_API_KEY; also supports Bedrock, Claude Platform on AWS, Vertex AI, and Azure. Anthropic does not permit claude.ai login for third-party agents built on the SDK.
    Managed Agents API (credits) Requires a Claude API key and the beta header; enabled by default for API accounts.

    One dated nuance is worth pinning down because it changes how subscription users pay for programmatic work. Starting June 15, 2026, Claude Agent SDK and claude -p usage on subscription plans no longer counts toward your Claude plan’s interactive usage limits; instead, eligible subscribers receive a separate monthly Agent SDK credit (per-user, not pooled), while subscription usage limits stay reserved for interactive use of Claude Code, Cowork, and Claude. If you use the Agent SDK with an API key from the Claude Platform, nothing changes — pay-as-you-go billing continues and you do not receive an Agent SDK monthly credit.

    SDK vs Managed Agents: the programmatic split

    Both programmatic surfaces let Claude run tools autonomously, but they differ in where the loop and the work live. Anthropic’s own comparison frames it this way: the Agent SDK “is a library that runs the agent loop inside your own process,” while Managed Agents “is a hosted REST API: Anthropic runs the agent and the sandbox, and your application sends events and streams back results.” Pick by who you want operating the infrastructure.

    Dimension Agent SDK Managed Agents
    Runs in Your process, your infrastructure Anthropic-managed infrastructure
    Interface Python or TypeScript library REST API
    Agent works on Files on your infrastructure A managed sandbox per session
    Session state JSONL on your filesystem Anthropic-hosted event log
    Best for Local prototyping; agents that work directly on your filesystem and services Production agents without operating sandbox/session infrastructure; long-running, asynchronous sessions

    A common path, per Anthropic’s docs, is to prototype with the Agent SDK locally, then move to Managed Agents for production.

    Quick chooser

    If you are not writing code and want Claude to finish a task on your computer, use Cowork. If you are a developer working interactively on a codebase, use Claude Code. If you are building your own agent and want it to run in your own process, use the Agent SDK. If you want Anthropic to run the agent and host the sandbox for long-running or production work, use Managed Agents.

    Is Claude Cowork the same as Claude Code?

    No. Both appear in the Claude desktop app, but Cowork is aimed at knowledge work (research, documents, spreadsheets, file management) for non-developers, while Claude Code is an agentic coding tool. Cowork runs only in the desktop app (macOS or Windows); Claude Code also runs in the terminal, VS Code, JetBrains, and the browser.

    Does a Claude subscription cover the Agent SDK or Managed Agents?

    Cowork and Claude Code are included with Claude subscriptions (Pro, Max, Team, Enterprise). The Agent SDK and Managed Agents are API surfaces authenticated with a Claude API key. As of June 15, 2026, subscription users do get a separate monthly Agent SDK credit for SDK and claude -p usage, but Managed Agents has no subscription path — it requires an API key and a beta header.

    Where does the work actually execute for each surface?

    Cowork runs on your own computer in the desktop app. Claude Code runs on your machine (or in the browser). The Agent SDK runs in your own process and infrastructure. Managed Agents executes in an Anthropic-managed cloud sandbox per session, or a self-hosted sandbox you control.

    Is the Agent SDK built on Claude Code?

    Yes. Per Anthropic, the Agent SDK “gives you the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript.” Anthropic also describes it as “Claude Code as a library.”

    Is Managed Agents generally available?

    No. As of June 13, 2026, Claude Managed Agents is in beta. Every Managed Agents endpoint requires the managed-agents-2026-04-01 beta header (the SDK sets it automatically), and access is enabled by default for API accounts.


  • Claude Enterprise Compliance: BAA, SOC 2, GDPR and Data Policy (2026)

    Claude Enterprise Compliance: BAA, SOC 2, GDPR and Data Policy (2026)

    Last verified: June 13, 2026

    Anthropic publishes a defined compliance posture for Claude: it holds SOC 2 Type I and Type II, ISO 27001:2022, and ISO/IEC 42001:2023 credentials; it will sign a Business Associate Agreement (BAA) covering HIPAA-ready services such as the first-party API and Enterprise plans; by default it does not train models on data sent under its commercial terms; and it offers a zero-data-retention (ZDR) arrangement on the Messages and Token Counting APIs. The hard part for buyers is the per-surface boundary — what the BAA covers, which features are blocked under ZDR or HIPAA, how long data is kept, and where it can be processed. Every figure below is drawn from Anthropic’s own trust, privacy, and developer documentation, with sources at the bottom. Eligibility, feature lists, and durations change; treat your signed contract and the live Trust Center as the controlling sources.

    Certifications and attestations

    Anthropic’s help center lists the following compliance credentials for its commercial products (Claude for Work and the Anthropic API). It directs customers to the Trust Portal at trust.anthropic.com to request copies of the underlying reports and certificates.

    Credential Status as described by Anthropic Scope
    SOC 2 Type I & Type II Listed as held Commercial products (Claude for Work, Anthropic API)
    ISO 27001:2022 Certified Information Security Management
    ISO/IEC 42001:2023 Certified (issued by Schellman Compliance, LLC, accredited by the ANSI National Accreditation Board) AI Management Systems
    HIPAA “HIPAA-ready configuration (BAA available)” See BAA section

    Anthropic describes itself as “one of the first frontier AI labs” to achieve ISO/IEC 42001:2023 certification, in an announcement dated January 13, 2025. The help-center certifications list does not mention ISO 27017, ISO 27018, FedRAMP, or CSA STAR; those are left out here rather than asserted. GDPR and CCPA are handled through Anthropic’s privacy program and customer agreements rather than as line-item “certifications” (see GDPR section).

    HIPAA and the BAA: covered by product surface

    Anthropic states it “provides a Business Associate Agreement (BAA) covering our HIPAA-ready services, such as use of our first-party API or Enterprise plans.” HIPAA readiness is enforced at the organization level: Anthropic provisions a dedicated HIPAA-enabled organization that automatically blocks non-eligible features. To process protected health information (PHI) on the API, an administrator must sign the BAA and contact sales to enable it; for Enterprise, an admin activates HIPAA compliance in the Claude Enterprise admin settings under “Data & Privacy” and signs the BAA there.

    Surface BAA / HIPAA-ready coverage
    First-party Claude API (Messages API) Covered as an Eligible Service (admin signs BAA, then contact sales)
    Claude Enterprise Covered once an admin activates HIPAA compliance and signs the BAA
    Workbench and Console Not covered
    Claude Free, Pro, Max, Team Not covered
    Cowork Not covered
    Claude Code Not covered under HIPAA readiness
    Amazon Bedrock / Vertex AI Not covered (cloud provider is the data processor; see those platforms)
    Claude Platform on AWS / Microsoft Foundry HIPAA readiness not available
    Beta features (e.g., Claude in Office, Claude Design) Generally not covered unless explicitly listed as eligible

    Within the API, only a subset of features is HIPAA-eligible. Anthropic enforces this in code: a HIPAA-enabled organization that sends a non-eligible feature gets a 400 invalid_request_error naming the blocked feature. Anthropic states your signed BAA is the official source of truth for what is covered.

    API feature HIPAA-eligible
    Messages API (/v1/messages) Yes
    Token counting Yes
    Web search Yes (dynamic filtering not eligible)
    Prompt caching, structured outputs, extended/adaptive thinking, citations, 1M context, PDF (inline), data residency, effort, fast mode, bash & text-editor tools, memory tool Yes
    Web fetch, computer use, advisor tool, context management (compaction / editing), tool search, cache diagnostics No
    Code execution, programmatic tool calling No
    Batch API, Files API, Agent Skills, MCP connector, Claude Managed Agents, MCP tunnels No

    PHI must appear only in message content, attached files, or related file names/metadata — never in JSON schema definitions (property names, enum/const values, or pattern regexes), because compiled schemas are cached separately and do not receive the same PHI protections. Anthropic notes workspace names, user contact details, billing data, and support tickets are not expected to contain PHI under the BAA.

    Data retention (commercial default)

    Under Anthropic’s commercial data retention policy, conversation content is not retained by default for the API, and API inputs and outputs are automatically deleted on the backend within 30 days of receipt or generation. For interface products such as Claude for Work, data persists until you delete it, after which it is removed from backend storage within 30 days. Two exceptions extend retention regardless of arrangement.

    Data type / event Retention
    API inputs and outputs (default) Auto-deleted within 30 days
    Deleted conversation content (Claude for Work) Removed from backend within 30 days
    Inputs/outputs for a chat flagged as a Usage Policy violation Up to 2 years
    Trust & safety classification scores (flagged chat) Up to 7 years
    Data tied to feedback you submit (thumbs up/down, bug report) 5 years

    Zero data retention (ZDR)

    With a ZDR arrangement, customer data is not stored at rest after the API response is returned, except where needed to comply with law or combat misuse. ZDR is requested through Anthropic sales and enabled per organization — it does not carry over automatically to new organizations under the same account. Even under ZDR, Anthropic retains User Safety classifier results, and may retain inputs and outputs for up to 2 years if a chat or session is flagged for a Usage Policy violation. CORS is not supported for ZDR organizations, so browser apps must call through a backend proxy.

    Surface ZDR coverage
    Claude Messages API & Token Counting API Eligible
    Claude Code (Commercial org API keys, or via Claude Enterprise with ZDR enabled) Eligible
    Console and Workbench Not eligible
    Claude Teams & Claude Enterprise interfaces Not eligible (except Claude Code via Enterprise with ZDR on)
    Claude Free, Pro, Max Not eligible
    Claude Managed Agents Not eligible (stateful; delete transcripts manually)
    Batch API, Files API, code execution, Agent Skills, MCP connector Not eligible
    Third-party integrations Not eligible

    A handful of ZDR-eligible features are marked “Yes (qualified)” — structured outputs and cache diagnostics — meaning Anthropic retains a narrow, documented set of technical data (for example, a cached JSON schema for up to 24 hours since last use) rather than your prompts or Claude’s outputs.

    Model-training policy and Covered Models

    Anthropic’s Privacy Policy states it does not apply to content processed on behalf of business customers; that data is governed by the customer agreement. For the API specifically, Anthropic states retained data is never used for model training without your express permission. Anthropic’s consumer-terms update confirms the data-use changes “do not apply to services under our Commercial Terms,” including Claude for Work, Claude for Government, Claude for Education, and API use (including via Amazon Bedrock and Google Cloud’s Vertex AI). Training on commercial data happens only if a customer explicitly opts in (for example, the Development Partner Program).

    One model-specific exception affects retention, not training: Claude Fable 5 and Claude Mythos 5 are designated Covered Models and require 30-day data retention. ZDR is not available for these two models; a request to either from an organization whose retention configuration doesn’t meet the requirement returns a 400 invalid_request_error. Organizations with ZDR can turn on 30-day retention for a single workspace (Console > Settings > Workspaces > Privacy controls) to use those models there while keeping ZDR elsewhere. On Bedrock, Vertex AI, and Microsoft Foundry, retention requirements for these models are set by each platform.

    GDPR, data residency, and international transfers

    For users in the EEA, UK, or Switzerland, the data controller is Anthropic Ireland, Limited; elsewhere it is Anthropic PBC. Where the EU or UK GDPR applies, Anthropic responds to verifiable data-subject requests within one calendar month. For transfers to countries without an adequacy decision, Anthropic relies on standard contractual clauses, and publishes its subprocessors at anthropic.com/subprocessors.

    On data residency, the Claude API exposes two independent controls. inference_geo sets where inference runs per request — values are "global" (default) or "us" — and is supported on Claude Opus 4.6, Sonnet 4.6, and later (older models return a 400). Workspace geo controls where data is stored at rest and where endpoint processing happens; it is set at workspace creation and cannot be changed afterward. Per Anthropic’s documentation, "us" is currently the only available workspace geo, and only "us" and "global" inference geos are available — so there is currently no EU-resident storage option at the workspace level. US-only inference is priced at 1.1x the standard rate on supported models. Data residency is available on the Claude API (first-party) and Claude Platform on AWS; on Bedrock and Vertex AI the region is set by the endpoint or inference profile.

    Does Anthropic train its models on my API or commercial data?

    No, not by default. Anthropic’s Privacy Policy excludes business-customer content (governed by your customer agreement), and for the API it states retained data is never used for training without your express permission. The consumer data-use changes explicitly do not apply to Commercial Terms services. Training on commercial data requires an explicit opt-in.

    Will Anthropic sign a BAA, and for what?

    Yes. Anthropic signs a BAA covering HIPAA-ready services such as the first-party API and Enterprise plans. The Messages API is covered as an Eligible Service. It does not cover Workbench/Console, Free/Pro/Max/Team, Cowork, Claude Code, or beta features unless explicitly listed. An admin must sign the BAA and enable HIPAA readiness; the organization then auto-blocks non-eligible features.

    What’s the difference between ZDR and HIPAA readiness?

    Per Anthropic, ZDR prevents customer data from being stored at rest after the API response. HIPAA readiness is a broader set of safeguards (encryption, access controls, audit logging) that protect PHI throughout its lifecycle and lets data be retained with safeguards rather than deleted immediately. Anthropic states you do not also need ZDR if you have HIPAA readiness.

    How long does Anthropic keep my data?

    By default, API inputs and outputs are auto-deleted within 30 days. If a chat is flagged as a Usage Policy violation, inputs/outputs may be retained up to 2 years and trust & safety classification scores up to 7 years. Data tied to feedback you submit is kept 5 years. ZDR removes the default at-rest storage but does not remove the law/misuse exceptions.

    Can I keep Claude inference and data in the EU?

    Not at rest currently. The API’s inference_geo can pin inference to "us" or run "global", but Anthropic’s documentation lists "us" as the only available workspace geo (storage region). EU/UK data-subject rights and standard contractual clauses apply regardless, but an EU storage-residency option is not currently offered at the workspace level per the docs verified here.


  • Claude Skills vs MCP vs Connectors vs Plugins: What Each One Is (2026)

    Claude Skills vs MCP vs Connectors vs Plugins: What Each One Is (2026)

    Last verified: June 13, 2026

    The simplest way to keep these straight: Skills teach Claude how to do a task, MCP servers and Connectors give Claude access to external systems, Plugins bundle several of these together, and Hooks and slash commands control a Claude Code session. A Skill is a folder of instructions Claude reads when relevant. MCP (the Model Context Protocol) is an open standard that connects Claude to your tools and data. A Connector is Anthropic’s packaging of a remote MCP server inside the Claude apps. A Plugin packages any combination of commands, agents, MCP servers, hooks, and skills for Claude Code. Every definition below is taken verbatim from Anthropic’s official documentation, fetched on the verification date.

    The one-glance comparison

    This is the liftable summary. Each row is one mechanism; the third column is the distinction people most often get wrong — whether the thing teaches Claude how to do something or gives Claude access to something.

    Type What it is Teaches-HOW or gives-ACCESS Where it runs How you install / enable it
    Skill A modular capability that packages instructions, metadata, and optional resources (scripts, templates) in a SKILL.md file that Claude uses automatically when relevant. Teaches HOW. Provides domain-specific expertise: workflows, context, and best practices (procedural knowledge). In Claude’s code execution environment / VM, where Claude has filesystem access, bash, and code execution. claude.ai: upload a zip under Settings > Features. API: upload via the Skills API (/v1/skills) with the required beta headers (code-execution-2025-08-25, skills-2025-10-02, files-api-2025-04-14). Claude Code: a SKILL.md directory under ~/.claude/skills/ or .claude/skills/.
    MCP server An implementation of the Model Context Protocol — “an open-source standard for connecting AI applications to external systems.” Described as “a USB-C port for AI applications.” Gives ACCESS. Connects Claude to data sources, tools, and workflows so it can access information and perform tasks. Local (stdio) servers run as processes on your machine; remote servers run over HTTP (recommended) or SSE (deprecated). In Claude Code: claude mcp add, at local, project, or user scope. Also configurable in .mcp.json or imported from Claude Desktop / claude.ai.
    Connector A feature that “let[s] Claude access your apps and services, retrieve your data, and take actions within connected services.” Custom connectors use remote MCP. Gives ACCESS. Same access role as MCP — a Connector is the in-app packaging of a remote MCP server. Custom connectors are reached from Anthropic’s cloud infrastructure, not from your local machine. In the Claude apps under Customize > Connectors (or the in-chat “+” menu). Add a directory connector, or “Add custom connector” by URL.
    Plugin “A lightweight way to package and share any combination of” Claude Code customizations. Both — it’s a container. Bundles things that teach HOW (commands, skills) and things that give ACCESS (MCP servers), plus hooks and subagents. In Claude Code — “they’ll work across your terminal and VS Code.” The /plugin command (public beta). For a marketplace: /plugin marketplace add user-or-org/repo-name, then install from the /plugin menu.
    Hook “User-defined shell commands, HTTP endpoints, or LLM prompts that execute automatically at specific points in Claude Code’s lifecycle.” Controls behavior. Provides deterministic control rather than relying on the LLM to decide. In Claude Code, firing at lifecycle events (e.g. PreToolUse, PostToolUse, UserPromptSubmit, SessionStart, Stop). Configured in JSON settings files such as ~/.claude/settings.json or .claude/settings.json, or bundled in a plugin.
    Slash command A command starting with / that controls a Claude Code session. Includes built-ins (e.g. /help, /compact) and custom commands. Teaches HOW (custom) / controls session (built-in). Custom commands have been merged into Skills. In the Claude Code session (terminal or VS Code). Built-ins ship with Claude Code. Custom: a Markdown file under .claude/commands/ (project) or ~/.claude/commands/ (personal); a .claude/skills/<name>/SKILL.md does the same.

    Skills: teaching Claude a procedure

    An Agent Skill is “a directory containing a SKILL.md file” with YAML frontmatter plus instructions, and optionally additional markdown files, executable scripts, and reference resources. The point is procedural knowledge — it turns “general-purpose agents into specialists” by giving Claude the workflows and best practices for a task, the way you’d write an onboarding guide for a new teammate. Anthropic ships pre-built Skills for PowerPoint, Excel, Word, and PDF, and you can author your own.

    What makes Skills cheap to install in bulk is progressive disclosure: Claude loads information in stages instead of all at once. The numbers below come straight from Anthropic’s Skills overview.

    Loading level When loaded Token cost (per Anthropic docs) Content
    Level 1: Metadata Always, at startup ~100 tokens per Skill name and description from the YAML frontmatter
    Level 2: Instructions When the Skill is triggered Under 5k tokens The SKILL.md body — workflows and guidance
    Level 3+: Resources As needed Effectively unlimited Bundled files read or executed via bash without loading their contents into context

    The name field is capped at 64 characters (lowercase letters, numbers, hyphens; it cannot contain the reserved words “anthropic” or “claude”), and the description is capped at 1,024 characters. One important constraint: custom Skills do not sync across surfaces — a Skill uploaded to claude.ai is not automatically available via the API, and Claude Code Skills are filesystem-based and separate from both.

    MCP: the open standard for access

    The Model Context Protocol is, in Anthropic’s words, “an open-source standard for connecting AI applications to external systems.” The canonical analogy: “Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.” Using MCP, “AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts).”

    An MCP server can expose three kinds of building block — tools, resources, and prompts. In Claude Code, resources are referenced with @server:protocol://resource/path and prompts surface as commands in the form /mcp__servername__promptname. You connect a server with claude mcp add, choosing a transport and a scope:

    Scope Loads in Shared with team Stored in
    Local (default) Current project only No ~/.claude.json
    Project Current project only Yes, via version control .mcp.json in project root
    User All your projects No ~/.claude.json

    For transports, HTTP is “the recommended option for connecting to remote MCP servers,” local stdio servers “run as local processes on your machine,” and SSE is explicitly marked deprecated in favor of HTTP.

    Connectors: MCP, packaged for the apps

    A Connector is how the Claude apps surface MCP. Per Anthropic’s help center, “Connectors let Claude access your apps and services, retrieve your data, and take actions within connected services,” and “Custom connectors using remote MCP are available on Claude, Cowork, and Claude Desktop.” So a Connector is not a different technology from MCP — a custom connector is a remote MCP server wired into the Claude UI.

    The most consequential detail is where the connection originates: “Custom connectors (remote MCP servers) are reached from Anthropic’s cloud infrastructure, not from your local machine.” That means a custom-connector MCP server must be reachable over the public internet — one hosted only on a private network, behind a VPN, or blocked by a firewall will not connect even if you can reach it yourself.

    Aspect Directory (pre-built) connector Custom connector
    Source Pre-built integrations in the Connectors Directory Added by you via a remote MCP server URL
    Plan availability Available across Claude plans Free, Pro, Max, Team, and Enterprise
    Free-plan limit Per directory “Free users are limited to one custom connector.”
    Where to add it Customize > Connectors, or the in-chat “+” > Connectors > Manage connectors

    Plugins: a bundle, not a single thing

    A Plugin is “a lightweight way to package and share any combination of” Claude Code customizations. The official announcement lists four bundle components, and the Claude Code documentation adds skills as a fifth thing a plugin can carry:

    Component What it adds
    Slash commands Custom shortcuts for frequently-used operations
    Subagents Purpose-built agents for specialized development tasks
    MCP servers Connections to tools and data sources through MCP
    Hooks Customizations of Claude Code’s behavior at key workflow points
    Skills Per the Claude Code docs, a plugin can include a skills/ directory; plugin skills use a plugin-name:skill-name namespace

    You install a plugin “directly within Claude Code using the /plugin command.” To pull from a marketplace — “curated collections where other developers can discover and install plugins” — you run /plugin marketplace add user-or-org/repo-name and then install from the /plugin menu. Plugins “work across your terminal and VS Code.” Plugins were announced on October 9, 2025, as a public beta for all Claude Code users.

    Hooks and slash commands: controlling the session

    The last two mechanisms aren’t about adding capability — they’re about controlling a Claude Code session. Hooks are “user-defined shell commands, HTTP endpoints, or LLM prompts that execute automatically at specific points in Claude Code’s lifecycle.” Their defining property is determinism: they provide deterministic control rather than relying on the LLM to make decisions. A PreToolUse hook can, for example, block a destructive rm -rf command regardless of what Claude intended. Hooks are configured in JSON settings files (such as ~/.claude/settings.json or a project’s .claude/settings.json) and fire at events including SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, and Stop.

    Slash commands start with / and control the session. Built-in commands like /help and /compact ship with Claude Code. Custom commands are Markdown files — a project command lives at .claude/commands/<name>.md and a personal one at ~/.claude/commands/<name>.md, with the file name becoming the command. As of the 2026 Claude Code docs, custom commands have been merged into Skills: a file at .claude/commands/deploy.md and a skill at .claude/skills/deploy/SKILL.md “both create /deploy and work the same way,” and existing .claude/commands/ files keep working.

    Is a Connector the same as an MCP server?

    Effectively yes, for custom connectors. Anthropic states “Custom connectors using remote MCP are available on Claude, Cowork, and Claude Desktop,” and that they “are reached from Anthropic’s cloud infrastructure.” A Connector is the Claude-app packaging of a remote MCP server; MCP is the underlying open standard.

    What’s the difference between a Skill and an MCP server?

    A Skill teaches Claude how to do a task — it “provide[s] Claude with domain-specific expertise: workflows, context, and best practices.” An MCP server gives Claude access to external systems — it connects Claude “to data sources, tools and workflows.” One is procedural knowledge; the other is a connection.

    Do Skills cost a lot of context tokens?

    Not until used. Per Anthropic’s docs, Level 1 metadata costs about 100 tokens per Skill and is always loaded; the full SKILL.md body (under 5k tokens) only loads when the Skill is triggered; and bundled resources are read on demand with effectively no upfront cost. This is the “progressive disclosure” design.

    What can a Claude Code plugin contain?

    “Any combination of” slash commands, subagents, MCP servers, and hooks, per the announcement; the Claude Code documentation adds that a plugin can also bundle a skills/ directory. You install one with the /plugin command, optionally from a marketplace added via /plugin marketplace add.

    Are custom slash commands still a thing?

    They still work, but they’ve been folded into Skills. The Claude Code docs state custom commands “have been merged into skills,” that existing .claude/commands/ files keep working, and that a command file and an equivalent SKILL.md both produce the same / command. Skills add optional extras like supporting files and automatic invocation.


  • Migrating Off Retired Claude Models: The Breaking-Change Checklist (2026)

    Migrating Off Retired Claude Models: The Breaking-Change Checklist (2026)

    Last verified: June 13, 2026

    Claude Opus 4 (claude-opus-4-20250514) and Claude Sonnet 4 (claude-sonnet-4-20250514) are deprecated and retire on June 15, 2026, after which requests to them return a 404. The official replacements are claude-opus-4-8 and claude-sonnet-4-6. But swapping the model string alone will break a working integration: depending on which target you choose, several request parameters that were valid on the May 2025 models now return a 400 error, and two changes alter behavior silently. This page maps each removed or changed parameter to the exact failure and the fix.

    One distinction governs the whole migration. The Opus path (to claude-opus-4-8) is the strict one: it removes temperature/top_p/top_k and manual thinking budgets entirely. The Sonnet path (to claude-sonnet-4-6) is gentler: it keeps sampling parameters (with the older “one of temperature or top_p, not both” rule) and still accepts budget_tokens as deprecated-but-functional. The one rule both paths share: assistant-turn prefills now return 400.

    The breaking-change matrix

    Each row is a change that breaks on at least one migration target. “Error” means the API rejects the request server-side (HTTP 400) even though the SDK request type still type-checks. “Silent” means no error — the behavior simply differs.

    Change On Opus 4.8 On Sonnet 4.6 Symptom Fix
    thinking: {type:"enabled", budget_tokens:N} 400 error (removed) Deprecated, still works 400 on Opus; cost/latency drift on Sonnet thinking: {type:"adaptive"} + output_config.effort
    temperature / top_p / top_k 400 error (removed) Keep only one of temperature or top_p 400 on Opus if any set; 400 on Sonnet if both set Remove on Opus; steer via prompt. Keep one on Sonnet
    Assistant-turn prefill (last message role:"assistant") 400 error 400 error Request rejected on both output_config.format (structured outputs) or system-prompt instruction
    thinking.display default Defaults to "omitted" Returns summarized text Reasoning text empty on Opus (silent) Set display: "summarized" on Opus
    Tokenizer New tokenizer (more tokens) Unchanged tokenizer Same text counts higher on Opus; max_tokens too tight Re-baseline with count_tokens; add headroom
    output_format (top-level) Deprecated API-wide Deprecated API-wide Works, but slated for removal Move to output_config: {format: {...}}

    Model ID swaps and retirement dates

    Retiring model Model ID Retires Replacement
    Claude Opus 4 claude-opus-4-20250514 (alias claude-opus-4-0) June 15, 2026 claude-opus-4-8
    Claude Sonnet 4 claude-sonnet-4-20250514 (alias claude-sonnet-4-0) June 15, 2026 claude-sonnet-4-6

    These are the original May 2025 models, not the later Opus 4.6 or Sonnet 4.5 releases. Use the exact replacement strings above — do not append a date suffix to claude-opus-4-8 or claude-sonnet-4-6 (they are dateless pinned snapshots).

    budget_tokens to adaptive thinking

    The Opus path removes the fixed thinking budget. thinking: {type:"enabled", budget_tokens:N} returns a 400 on claude-opus-4-8. The replacement is adaptive thinking — the model decides how much to think per request — with overall depth controlled by the effort parameter (low | medium | high | xhigh | max). There is no direct token-count equivalent; effort is an output-level control, not a thinking budget.

    # Before (Claude Opus 4 / Sonnet 4)
    client.messages.create(
        model="claude-opus-4-20250514",
        max_tokens=16000,
        thinking={"type": "enabled", "budget_tokens": 10000},
        messages=[{"role": "user", "content": "..."}],
    )
    
    # After (Claude Opus 4.8)
    client.messages.create(
        model="claude-opus-4-8",
        max_tokens=16000,
        thinking={"type": "adaptive"},
        output_config={"effort": "high"},  # or "max", "xhigh", "medium", "low"
        messages=[{"role": "user", "content": "..."}],
    )

    On the Sonnet path, budget_tokens is deprecated but still functional on claude-sonnet-4-6, so it will not 400 — but you should still migrate to adaptive thinking. Note also that Sonnet 4.6 defaults to effort: "high" where Sonnet 4 had no effort parameter at all; if you do not set it explicitly you may see higher latency and token use after the swap.

    Sampling parameters: removed vs. restricted

    This is where the two paths diverge most. On claude-opus-4-8, setting temperature, top_p, or top_k to any non-default value returns a 400. Remove them entirely and steer behavior through prompting instead. (If you used temperature=0 for determinism, note it never guaranteed identical outputs on prior models either.)

    # Opus path — sampling params 400 on claude-opus-4-8
    # Before
    client.messages.create(
        model="claude-opus-4-20250514",
        temperature=0.7,
        top_p=0.9,
        messages=[...],
    )
    
    # After — remove them
    client.messages.create(
        model="claude-opus-4-8",
        messages=[...],
    )

    On claude-sonnet-4-6 the older Claude 4.x rule still applies: you may pass one of temperature or top_p, but passing both returns a 400. So a Sonnet 4 to Sonnet 4.6 move only requires dropping one of the two if you were setting both.

    Assistant-turn prefills to structured outputs

    Prefilling the final assistant turn — ending your messages array with a role: "assistant" message to force a response shape — returns a 400 on both claude-opus-4-8 and claude-sonnet-4-6. This is the one breaking change you cannot dodge by choosing the gentler target. The replacement depends on what the prefill was doing.

    Prefill was used for Replacement
    Forcing JSON / YAML / schema output output_config.format with a json_schema
    Forcing a classification label A tool with an enum field, or structured outputs
    Skipping preambles (“Here is…”) System-prompt instruction: respond directly, no preamble
    Continuing an interrupted response Move continuation into the user turn
    Steering around bad refusals Usually unnecessary now — plain user-turn prompting suffices
    # Before (fails on both targets) — prefill forcing JSON shape
    messages=[
        {"role": "user", "content": "Extract the name."},
        {"role": "assistant", "content": "{\"name\": \""},
    ]
    
    # After — structured outputs replace the prefill
    client.messages.create(
        model="claude-opus-4-8",
        max_tokens=1024,
        output_config={"format": {"type": "json_schema", "schema": SCHEMA}},
        messages=[{"role": "user", "content": "Extract the name."}],
    )

    Thinking display: the silent one

    On claude-opus-4-8, thinking blocks still stream, but their thinking text field is empty unless you opt in — the default is display: "omitted". There is no error; if your UI rendered the summarized reasoning, it now shows a long pause before output. Restore it by setting the display mode:

    thinking = {
        "type": "adaptive",
        "display": "summarized",  # default is "omitted" on Opus 4.8/4.7
    }

    The block-field name is unchanged — it is still block.thinking on a thinking-type block. The fix is the request parameter, not the response-handling code. (Sonnet 4.6 is not affected by this default change.)

    The new tokenizer: re-baseline max_tokens

    This change is Opus-only and easy to miss because it produces no error. claude-opus-4-8 uses the tokenizer introduced with Opus 4.7, under which the same text tokenizes to roughly 1x–1.35x as many tokens — up to about 35% more, around 30% on typical content, varying by workload. Three consequences:

    What to check Why
    max_tokens ceilings and compaction triggers The same output now consumes more tokens; tight limits truncate mid-thought
    Client-side token estimators (e.g. fixed char-to-token ratios) Calibrated against the old tokenizer; now undercount
    Cost and rate-limit dashboards count_tokens returns higher numbers; re-baseline before reacting

    Re-run client.messages.count_tokens(model="claude-opus-4-8", ...) on a representative sample of your prompts. Do not apply a blanket multiplier. Sonnet 4.6 keeps the older tokenizer, so a Sonnet 4 to Sonnet 4.6 move has no tokenizer re-baseline to do.

    The full checklist

    Step Opus 4 to 4.8 Sonnet 4 to 4.6
    Update model ID string Required Required
    Replace budget_tokens with adaptive thinking Required (400) Recommended (deprecated)
    Sampling params Remove all (400) Keep only one (both 400)
    Remove assistant-turn prefills Required (400) Required (400)
    Set display: "summarized" if showing reasoning Required for visible thinking Not applicable
    Re-baseline max_tokens for new tokenizer Required Not applicable
    Set effort explicitly Defaults to high Defaults to high
    Move output_format to output_config.format Recommended Recommended
    Verify tool inputs parsed with a JSON parser Recommended Recommended
    Spot-check one request, then roll out Required Required

    If you run Claude Code, /claude-api migrate applies the model swap, breaking-parameter changes, prefill replacement, and effort calibration across a codebase, then produces a verify-it-yourself checklist. It asks you to confirm scope before editing any files.

    Is migrating off Claude Opus 4 really not just a model-string change?

    No. Moving to claude-opus-4-8 also requires removing temperature/top_p/top_k and any budget_tokens (all now return 400), removing assistant-turn prefills (400), opting back into summarized thinking if your UI shows it, and re-baselining max_tokens for the new tokenizer. Only the Sonnet 4 to Sonnet 4.6 move is close to a drop-in — and even that requires removing prefills.

    When exactly do Claude Opus 4 and Sonnet 4 stop working?

    June 15, 2026. After that date, requests to claude-opus-4-20250514 and claude-sonnet-4-20250514 return a 404. These are the original May 2025 models, not Opus 4.6 or Sonnet 4.5.

    What replaces budget_tokens now that it errors on Opus?

    Adaptive thinking (thinking: {type:"adaptive"}) plus the effort parameter inside output_config. There is no exact token-count equivalent: the model decides how much to think per request, and effort (low through max) tunes overall depth and spend. On Sonnet 4.6, budget_tokens still works but is deprecated.

    Why does the same prompt cost more tokens on Opus 4.8?

    Opus 4.8 uses the tokenizer introduced with Opus 4.7, under which the same text produces roughly 1x–1.35x as many tokens (about 30% more on typical content, up to ~35%). Re-run the count_tokens endpoint against claude-opus-4-8 and give max_tokens and compaction triggers extra headroom. Sonnet 4.6 keeps the older tokenizer, so it is unaffected.

    My thinking summaries disappeared after migrating to Opus — is that a bug?

    No. On Opus 4.8 (and 4.7), thinking.display defaults to "omitted", so thinking blocks stream with an empty text field. Set display: "summarized" in your thinking config to restore visible reasoning. The field name is unchanged; only the default flipped.


  • Latest Claude Models — June 2026 (Current Lineup, Pricing, and Specs)

    Latest Claude Models — June 2026 (Current Lineup, Pricing, and Specs)

    Updated June 12, 2026. Fable 5 is the current top-tier model, released June 9, 2026. The full lineup: Fable 5 → Opus 4.8 → Sonnet 4.6 → Haiku 4.5. Pricing and availability verified against Anthropic’s official docs.

    Current Claude Models — Quick Reference

    Model API ID Input $/MTok Output $/MTok Context Best For
    Claude Fable 5 🆕 claude-fable-5 $10.00 $50.00 1M tokens Complex engineering, long-horizon agentic work
    Claude Opus 4.8 claude-opus-4-8 $5.00 $25.00 1M tokens Everyday advanced work, high-volume pipelines
    Claude Sonnet 4.6 claude-sonnet-4-6 $3.00 $15.00 1M tokens Balanced capability and speed
    Claude Haiku 4.5 claude-haiku-4-5-20251001 $1.00 $5.00 200K tokens High-speed, cost-sensitive tasks

    All four models support vision, tool use/function calling, and batch processing. Fable 5 and Opus 4.8 are available on AWS Bedrock, Google Cloud Vertex AI, and Microsoft Azure AI Foundry in addition to the direct Anthropic API.

    Claude Fable 5 (June 9, 2026)

    Fable 5 is Anthropic’s first publicly available Mythos-class model — a capability tier previously restricted to research and select enterprise partners. It’s the most capable model Anthropic has released to date.

    What makes Fable 5 different:

    • SWE-bench Verified: 95.0% (vs 88.6% for Opus 4.8)
    • SWE-bench Pro: 80.0% (vs 69.2%)
    • Senior Engineer benchmark: 91/100 (vs ~63/100)
    • Adaptive extended thinking (always on, not a mode switch)

    Important limitations:

    • 2x the cost of Opus 4.8 ($10/$50 vs $5/$25)
    • Mandatory 30-day data retention — not available under zero data retention (ZDR)
    • Safety classifiers route cybersecurity, biology, chemistry, and distillation prompts to an Opus 4.8 fallback — you pay Fable 5 rates for Opus 4.8 output in those domains
    • Higher latency on complex tasks (60 seconds to several minutes vs 3–15 seconds for Opus 4.8)

    Free through June 22, 2026: Claude Pro, Max 5x, Max 20x, Team, and Enterprise subscription plans include Fable 5 at no extra charge during the launch window.

    Claude Opus 4.8

    Opus 4.8 is Anthropic’s current workhorse for serious work — the right default for most API applications and Claude Code use. It supports zero data retention (ZDR), which Fable 5 does not.

    Key specs:

    • Context: 1M tokens
    • Max output: 32K tokens per request
    • Extended thinking: Available (opt-in mode)
    • ZDR: Yes
    • Batch API: Yes (50% discount on batch processing)

    Use Opus 4.8 as your default model unless you have a specific reason to go up to Fable 5 or down to Sonnet/Haiku. It hits the best balance of capability, speed, cost, and data policy flexibility.

    Claude Sonnet 4.6

    Sonnet 4.6 targets use cases where response speed matters and you don’t need Opus-level reasoning. It’s the model Anthropic’s own infrastructure runs Claude.ai on for Pro subscribers doing day-to-day chat work.

    Key specs:

    • Context: 1M tokens
    • Max output: 64K tokens per request
    • Extended thinking: Available
    • ZDR: Yes
    • Typical latency: 1–5 seconds for most tasks

    Good for: content generation pipelines, customer-facing chat, document analysis at volume, anything where sub-5-second response time matters.

    Claude Haiku 4.5

    Haiku 4.5 is Anthropic’s fastest and cheapest model. At $1 input / $5 output per million tokens, it’s 10x cheaper than Fable 5.

    Key specs:

    • Context: 200K tokens (smaller than the Opus/Sonnet/Fable 1M window)
    • Max output: 16K tokens per request
    • Latency: Sub-second for most tasks
    • ZDR: Yes

    The 200K context window is the main limitation. For tasks that fit within that window — classification, short-form generation, routing, extraction — Haiku 4.5 is the cost-optimal choice. For longer documents or conversations, step up to Sonnet or Opus.

    How to Choose the Right Claude Model

    The decision framework I use:

    1. Does the task require multi-step reasoning, complex coding, or long-horizon autonomy? → Fable 5 (if cost and latency are acceptable) or Opus 4.8
    2. Is this a routine task at reasonable volume? → Opus 4.8 as the default
    3. Does latency matter more than maximum reasoning depth? → Sonnet 4.6
    4. Is this high-volume, short-context, cost-sensitive work? → Haiku 4.5
    5. Does your use case require zero data retention? → Any model except Fable 5

    Most production applications use a routing strategy: Fable 5 or Opus 4.8 for the hard jobs, Haiku 4.5 for classification and pre-processing, Sonnet 4.6 for user-facing response generation.

    Claude Subscription Plans and Model Access (June 2026)

    Plan Price Models Included
    Free $0 Limited Sonnet 4.6 access
    Pro $20/mo ($17 annual) Sonnet 4.6, Opus 4.8, Fable 5 (through June 22)
    Max 5x $100/mo All models, 5x usage vs Pro
    Max 20x $200/mo All models, 20x usage vs Pro
    Team Standard $20/seat/mo (annual), $25 month-to-month All models + admin features
    Team Premium $100/seat/mo (annual), $125 month-to-month All models + priority + advanced admin
    Enterprise $20/seat + usage at API rates (contact sales) All models + ZDR + custom retention + SSO

    Claude Code (the CLI tool) is included in all paid subscription plans. API access for building your own applications is separate — billed per token via the Anthropic Console regardless of subscription status.

    Legacy Models (Still Available, No Longer Latest)

    These models are still available via the API but are not Anthropic’s current recommended versions:

    • Claude Opus 4.7 (claude-opus-4-7) — prior Opus tier, succeeded by 4.8
    • Claude Opus 4.6 (claude-opus-4-6) — two generations back
    • Claude Sonnet 4.5 (claude-sonnet-4-5) — prior Sonnet tier
    • Claude 3.5 Haiku / Sonnet / Opus — Claude 3.x generation, still functional for legacy integrations

    If you’re building a new application, start with the current lineup. Legacy model IDs are useful for maintaining compatibility in existing applications that haven’t been updated.

    Platform Availability

    Platform Fable 5 Opus 4.8 Sonnet 4.6 Haiku 4.5
    Anthropic API (direct)
    AWS Bedrock
    Google Cloud Vertex AI
    Microsoft Azure AI Foundry
    GitHub Copilot ✓ (via Foundry)

    Frequently Asked Questions

    What is the newest Claude model?
    As of June 2026, Claude Fable 5 is the newest and most capable model Anthropic has released. It launched June 9, 2026. The API model ID is claude-fable-5.

    Is Claude Fable 5 the same as Claude 5?
    No. Anthropic changed the naming convention — there is no “Claude 5.” The Fable series is a new tier above the Opus/Sonnet/Haiku hierarchy. Fable 5 is Anthropic’s first Mythos-class model released for general availability.

    What is the most powerful Claude model?
    Claude Fable 5 is currently the most powerful. For tasks where Fable 5’s safety classifier routing applies (cybersecurity, biology, chemistry, distillation), or where zero data retention is required, Claude Opus 4.8 is the appropriate top-tier choice.

    What Claude model does Claude.ai use by default?
    Depends on your plan. Free tier uses a limited version of Sonnet. Pro and Max subscribers access Opus 4.8 as the default with Fable 5 available (through June 22, 2026 included, after that plan-dependent). Claude.ai routes to the appropriate model for your plan automatically.

    How do I use the latest Claude model in the API?
    Set the model parameter in your API request to the model ID. For Fable 5: "model": "claude-fable-5". For Opus 4.8: "model": "claude-opus-4-8". See the full API reference at console.anthropic.com.

    What’s the difference between Claude Opus 4.8 and Fable 5?
    Fable 5 is significantly stronger on complex engineering tasks — SWE-bench Pro: 80% vs 69.2%, and Senior Engineer benchmark: 91 vs ~63 out of 100. The trade-off: Fable 5 costs 2x more ($10/$50 vs $5/$25 per MTok), has higher latency, and requires 30-day data retention. For most work, Opus 4.8 is the right choice. Full Fable 5 vs Opus 4.8 breakdown here.

    Changelog

    • June 12, 2026 — Added Claude Fable 5 (released June 9). Updated pricing table. Added platform availability table.
    • May 2026 — Claude Opus 4.8 and Sonnet 4.6 were the current top-tier models.

    This page is updated as Anthropic releases new models. Last verified: June 12, 2026. For API pricing, check console.anthropic.com — the canonical source.

  • Claude Code Getting Started: Installation, First Run, and the 5 Commands You’ll Use Daily

    Claude Code Getting Started: Installation, First Run, and the 5 Commands You’ll Use Daily

    Claude Code is Anthropic’s official CLI for Claude — a terminal-based agent you can point at any codebase and have it read, write, test, and ship code. It’s different from the Claude.ai chat interface in one key way: Claude Code can act, not just answer. It reads your actual files, runs your actual commands, and makes changes that stick.

    This guide walks you through installation, first run, and the commands that cover 90% of what you’ll do daily.

    What Claude Code Is (and Isn’t)

    Claude Code runs in your terminal. It gives Claude access to your local machine — file system, shell, and any MCP servers you configure — so it can do real engineering work: implement features, fix bugs, write tests, explain unfamiliar codebases, and run multi-step agentic workflows.

    It is not a code autocomplete plugin (that’s what GitHub Copilot does). Claude Code is a conversational agent that works at the task level, not the token level. You describe what you want; it figures out the steps and executes them.

    Installation

    Claude Code requires Node.js 18 or later. Install via npm:

    npm install -g @anthropic-ai/claude-code

    Verify the install:

    claude --version

    That’s the only dependency. Claude Code is a Node.js CLI — no Docker, no Python env, no platform-specific setup beyond Node.

    First-Run Authentication

    The first time you run claude, it walks you through authentication. You have two options:

    Option 1: Claude subscription (Pro, Max, Team, Enterprise)
    Run claude, select “Login with Claude.ai,” and it opens a browser window to authorize. Your subscription covers Claude Code usage — no separate API billing.

    Option 2: Anthropic API key
    Set your API key as an environment variable before running:

    export ANTHROPIC_API_KEY="sk-ant-..."
    claude

    Or on Windows:

    $env:ANTHROPIC_API_KEY = "sk-ant-..."
    claude

    API key usage is billed per token at standard API rates. For heavy daily use, a Max subscription ($100–$200/month) is usually more economical than API billing.

    Your First Session

    Navigate to a project directory and start Claude Code:

    cd ~/projects/my-app
    claude

    Claude Code reads your directory automatically. At the > prompt, describe what you want:

    > What does this codebase do? Give me a 3-paragraph overview.
    

    Claude reads the files it needs and responds. No configuration required for basic usage — Claude Code infers context from the directory you’re in.

    The 5 Commands You’ll Use Daily

    1. claude — Start an interactive session

    claude

    Launches the REPL (read-eval-print loop). This is where you spend most of your time. Claude has access to your current directory’s files, can run bash commands, and can call any MCP servers you’ve configured.

    Within a session, you can:

    • Ask questions about the codebase
    • Request implementations (“add a rate limiter to the auth middleware”)
    • Have Claude run tests and fix failures
    • Use /help to see available slash commands
    • Use /clear to reset context without leaving the session
    • Press Escape twice to interrupt a running task

    2. claude -p "prompt" — One-shot non-interactive mode

    claude -p "What are all the API endpoints in this codebase?"

    Runs a single prompt and exits. No REPL. Good for scripting, CI pipelines, or quick one-off queries you don’t want to interrupt a workflow for. Output goes to stdout — pipe it wherever you need it.

    claude -p "Summarize the changes in the last 10 commits" | pbcopy

    3. claude mcp add — Connect an external tool

    claude mcp add github -- npx -y @modelcontextprotocol/server-github

    Adds an MCP server to your Claude Code configuration. After running this, Claude can call the server’s tools in any session. Common additions:

    # File system access (scoped to a directory)
    claude mcp add files -- npx -y @modelcontextprotocol/server-filesystem ~/Documents
    
    # GitHub integration
    claude mcp add github -- npx -y @modelcontextprotocol/server-github
    
    # Web search
    claude mcp add search -- npx -y @modelcontextprotocol/server-brave-search

    The GitHub and Brave Search servers need API tokens — set them as environment variables before the server starts, or pass them via the --env flag in the mcp add command.

    4. claude -c — Continue the last conversation

    claude -c

    Resumes your most recent Claude Code conversation, including all prior context. Essential for multi-session work on a feature. If you closed the terminal mid-task, claude -c picks up exactly where you left off.

    For a specific prior conversation:

    claude --resume SESSION_ID

    5. claude --model — Select the model for a session

    claude --model claude-opus-4-8

    Claude Code defaults to the most capable available model for your plan. You can override this per session. Current options:

    • claude-fable-5 — Highest capability, complex tasks (2x cost vs Opus 4.8)
    • claude-opus-4-8 — Default for most work, strong balance of quality and speed
    • claude-sonnet-4-6 — Faster responses, good for routine tasks
    • claude-haiku-4-5-20251001 — Fastest, lowest cost, short tasks

    Slash Commands Inside a Session

    While in a Claude Code session (> prompt), these slash commands are available:

    Command What It Does
    /help Show all available commands
    /clear Clear conversation context (keep the session open)
    /compact Compress prior context to save tokens while preserving essential memory
    /cost Show token usage and estimated cost for the current session
    /model Switch the model mid-session
    /review Request a multi-agent code review of the current branch
    /init Generate a CLAUDE.md file with project context for this repo
    /exit End the session

    CLAUDE.md — Project-Level Context

    Drop a CLAUDE.md file in your project root and Claude Code reads it automatically at session start. Use it to encode project-specific context Claude shouldn’t have to re-derive every session:

    # My Project
    
    ## Architecture
    - Backend: FastAPI + PostgreSQL
    - Frontend: React + TypeScript
    - Deployed to: AWS ECS
    
    ## Development
    - Tests: `pytest tests/`
    - Local server: `./scripts/start-dev.sh`
    - Database migrations: `alembic upgrade head`
    
    ## Rules
    - Never modify migration files directly
    - All API routes go in `src/routes/`
    - Use `httpx` not `requests` for HTTP calls

    Generate a starter CLAUDE.md for an existing project with /init.

    Permission Modes

    Claude Code asks for confirmation before running bash commands, creating files, or making other changes — unless you grant it broader permissions. There are three ways to control this:

    • Default: Claude asks before each tool use that modifies files or runs commands
    • --dangerously-skip-permissions: Skip all confirmations. Use only in isolated environments (Docker containers, CI). Not for everyday use on your primary machine.
    • Session-level allowlist: During a session, you can approve individual tools for the rest of the session by selecting “Allow always” when prompted

    For most work, the default confirmation behavior is the right trade-off — it keeps you in the loop on changes without requiring you to pre-define a permission policy.

    IDE Integration

    Claude Code integrates with VS Code and JetBrains IDEs. Install the extension from each marketplace, then launch Claude Code from inside the IDE. This keeps the terminal panel visible alongside your editor without alt-tabbing between windows.

    The IDE extensions also add shortcuts for common actions like opening Claude Code in the current file’s directory and running one-shot queries against the selected code.

    Frequently Asked Questions

    What’s the difference between Claude Code and Claude.ai?
    Claude.ai is the web chat interface — good for questions, document analysis, and writing. Claude Code is a terminal CLI that can access your local files, run commands, and act autonomously on multi-step tasks. Claude.ai can’t modify files on your machine; Claude Code can.

    Does Claude Code cost extra on top of my Claude subscription?
    No. Claude Pro, Max, Team, and Enterprise subscriptions include Claude Code access. You use the same account. Heavy agentic usage counts toward the plan’s usage limits, but there’s no separate Claude Code fee.

    Can Claude Code access the internet?
    Not by default. Claude Code’s built-in WebFetch tool can fetch content from a specific URL when you provide it. For live web search, add the Brave Search or similar MCP server. Claude can’t browse freely without explicit tool access.

    What does Claude Code do with my code?
    Claude Code sends the file contents and context it needs to the Anthropic API for inference. Standard Anthropic API data policies apply — if you’re using an API key, you can configure zero data retention. If you’re using a subscription, default Anthropic retention policies apply. Review Anthropic’s privacy policy for current details.

    Is Claude Code open source?
    Claude Code itself (the CLI client) is not open source — it’s an Anthropic product. The MCP server ecosystem it connects to includes many open-source servers, and the MCP specification itself is open.

    What version of Node.js do I need?
    Node.js 18 or later. Run node --version to check. The Long-Term Support (LTS) version is always a safe choice.

    Last verified: June 12, 2026. Claude Code is updated frequently — run npm update -g @anthropic-ai/claude-code to stay current.

  • What Is Model Context Protocol (MCP)? The Complete Guide for Claude Users

    What Is Model Context Protocol (MCP)? The Complete Guide for Claude Users

    Model Context Protocol (MCP) is the reason Claude can read your files, query your database, search the web, and push code to GitHub — all from inside a single conversation. Without it, Claude would be limited to whatever you paste in manually. With it, Claude connects to almost any external system.

    Quick answer: MCP is an open standard developed by Anthropic that lets AI models securely connect to external tools, data sources, and services through a standard client-server architecture. You install an MCP server for the system you want Claude to access. Claude becomes a client that calls that server. The server executes the action and returns results.

    The Problem MCP Solves

    Before MCP, connecting an AI model to external data meant one of two things: either the AI company built a native integration (slow, expensive, proprietary), or you cobbled together a pipeline that passed data manually between systems.

    Neither approach scales. If Claude natively supported every database, every API, every file format, and every SaaS tool on the planet, the model would be perpetually behind. And manual copy-paste workflows aren’t agentic — they require you to do all the coordination work the AI should be doing.

    MCP solves this with a universal adapter layer. Instead of building individual integrations, Anthropic defined a standard. Now any developer can build an MCP server for any system, and any MCP-compatible AI client (like Claude) can use it automatically.

    How MCP Works

    MCP uses a client-server model over two transport mechanisms:

    • stdio: The MCP server runs as a local subprocess on your machine. Claude Code spawns it, communicates via standard input/output. This is the most common setup.
    • HTTP/SSE: The MCP server runs as a network service. Claude connects over HTTP with Server-Sent Events for streaming. Better for remote or shared servers.

    The communication protocol underneath is JSON-RPC 2.0 — a lightweight, well-understood standard for calling methods and getting results.

    Each MCP server exposes one or more of three primitives:

    • Tools: Functions Claude can call. Example: read_file(path), create_issue(title, body), run_query(sql). Claude decides when to call them based on context.
    • Resources: Data sources Claude can read. Example: the contents of a directory, a database schema, a project’s README. Resources are passive — they don’t take actions, they expose information.
    • Prompts: Reusable prompt templates that servers can provide to standardize how Claude interacts with them.

    When Claude sees a task that could benefit from an available tool, it calls the tool, receives the result, and incorporates it into the response. This happens automatically — you don’t have to tell Claude when to use MCP. Claude decides based on what the server exposes.

    MCP in Claude Code vs Claude Desktop

    Both Claude Code (the CLI tool) and Claude Desktop support MCP, but they configure servers differently.

    Claude Code

    Claude Code has built-in MCP management via the claude mcp command family:

    claude mcp add my-server -- npx -y @modelcontextprotocol/server-filesystem /path/to/directory
    claude mcp list
    claude mcp remove my-server

    Servers added with claude mcp add are stored in your Claude Code config (~/.claude.json or the project-level .claude/settings.json). Project-level configs let you commit MCP server setups to source control so the whole team gets them automatically.

    Claude Code also ships with a set of built-in tools that behave like MCP servers but don’t require separate installation: file read/write/edit, bash execution, glob search, grep, web fetch, and the agent spawning tools you’re reading about in this article.

    Claude Desktop

    Claude Desktop reads MCP server configuration from a JSON file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json

    A typical config entry looks like this:

    {
      "mcpServers": {
        "filesystem": {
          "command": "npx",
          "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/Documents"]
        },
        "github": {
          "command": "npx",
          "args": ["-y", "@modelcontextprotocol/server-github"],
          "env": {
            "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token_here"
          }
        }
      }
    }

    Restart Claude Desktop after editing the config. Each server you add appears in the Claude Desktop interface with a hammer icon, and Claude can access its tools in any conversation.

    The Most Useful MCP Servers

    Anthropic maintains a reference set of official MCP servers. These are the ones worth knowing:

    Server What It Does Package
    Filesystem Read/write files and directories on your local machine @modelcontextprotocol/server-filesystem
    GitHub Read repos, create issues, open PRs, push code @modelcontextprotocol/server-github
    PostgreSQL Read-only SQL queries against a Postgres database @modelcontextprotocol/server-postgres
    SQLite Read/write a local SQLite database file @modelcontextprotocol/server-sqlite
    Brave Search Live web search via Brave’s Search API @modelcontextprotocol/server-brave-search
    Puppeteer Headless browser — screenshot pages, scrape, fill forms @modelcontextprotocol/server-puppeteer
    Slack Read channels, send messages, search workspace @modelcontextprotocol/server-slack
    Google Drive Read and search Google Drive files @modelcontextprotocol/server-google-drive
    Git Git operations — log, diff, commit, branch management @modelcontextprotocol/server-git
    Memory Persistent key-value knowledge graph across conversations @modelcontextprotocol/server-memory

    Beyond the official set, hundreds of community-built MCP servers cover everything from Notion and Linear to AWS and Docker. The MCP ecosystem grew faster than almost anyone expected after the November 2024 launch.

    Installing Your First MCP Server

    The fastest path is Claude Code with the filesystem server. This gives Claude read/write access to a directory you specify — useful for any project work.

    Prerequisites: Node.js installed (the server runs via npx).

    In your terminal:

    claude mcp add filesystem -- npx -y @modelcontextprotocol/server-filesystem ~/Documents/projects

    That’s it. Open a Claude Code session. Claude can now list, read, write, and search files inside ~/Documents/projects. Try: “List all Python files in this directory and summarize what each one does.”

    For Claude Desktop, edit the claude_desktop_config.json file directly (see format above), then restart the app.

    What MCP Cannot Do

    A few things worth understanding before you build on MCP:

    MCP servers don’t persist between conversations. Each Claude session starts fresh. If you need state persistence, you need a server with its own storage layer (the Memory server handles this specifically).

    MCP doesn’t bypass Claude’s safety guidelines. Claude still decides whether to execute a tool call based on safety and ethics reasoning. Connecting a filesystem server doesn’t give Claude unlimited license to delete files — Claude will still confirm before destructive operations.

    Subprocess MCP servers are local. The stdio transport runs servers on your machine. This means they only work when you’re running Claude Code locally. For remote or team-shared access, you need HTTP/SSE transport with a hosted server.

    Security Considerations

    MCP servers have real permissions. The filesystem server can read and write files. The GitHub server can push code to your repos. The Postgres server can run SQL queries.

    Apply the principle of least privilege:

    • Scope filesystem servers to the directory you actually need, not /
    • Use read-only database credentials where you don’t need writes
    • Create GitHub tokens with minimum required scope (e.g., repo for private repos, not org-level admin)
    • Never commit environment variables containing API keys to source control, even in .claude/settings.json — use env var references instead

    MCP servers run with the permissions of the user running Claude. If something goes wrong with a tool call, it can have real consequences. The upside: everything runs locally and through your own credentials — there’s no MCP cloud intermediary with access to your data.

    MCP and Claude Code’s Agentic Workflows

    The full power of MCP shows up in Claude Code’s multi-step agentic mode. When Claude Code has access to git, a filesystem, a browser, and a search tool simultaneously, it can execute workflows like:

    1. Search the web for a library’s current API (Brave Search)
    2. Read your existing code to understand the integration point (filesystem)
    3. Write the updated code (filesystem write)
    4. Run tests (bash)
    5. Create a PR (GitHub)

    Each of these steps would require a separate tool in a traditional automation stack. With MCP, Claude orchestrates all of them within a single session, using whatever servers are available.

    This is what makes MCP the infrastructure layer for agentic AI — not a feature, but the foundation that makes complex AI-driven workflows possible.

    Frequently Asked Questions

    What does MCP stand for?
    Model Context Protocol. It’s an open standard for connecting AI models to external tools, data sources, and services through a standard client-server interface.

    Who created MCP?
    Anthropic created MCP and released it as an open standard in November 2024. The specification and reference servers are open-source on GitHub. While Claude is the primary client, other AI systems can implement MCP clients too.

    Do I need to install MCP to use Claude?
    No. Claude works without any MCP servers. MCP is an extension layer — you add servers when you want Claude to access specific external systems. Claude Code also ships with a set of built-in tools (file operations, bash, web fetch) that don’t require MCP installation.

    Is MCP available on Claude.ai (the web app)?
    MCP server support is primarily in Claude Desktop and Claude Code. The Claude.ai web interface has its own tool integrations (web search, document analysis) but doesn’t support custom MCP servers in the same way.

    What’s the difference between MCP tools and Claude’s native tools in Claude Code?
    Claude Code’s native tools (Read, Write, Bash, Glob, Grep, WebFetch, Agent) are built into the application and don’t require a separate server process. MCP servers are external — they run as subprocesses or network services that Claude Code connects to. Both expose tools that Claude can call; the mechanism for loading them is different.

    How do I build my own MCP server?
    Anthropic provides official SDKs for building MCP servers in TypeScript, Python, Go, and other languages. The TypeScript SDK (@modelcontextprotocol/sdk) is the most mature. Start with Anthropic’s MCP documentation and the reference server implementations on GitHub as templates.

    Last verified: June 12, 2026. MCP specification and server ecosystem evolve quickly — check the official Anthropic MCP documentation for the current spec.

  • Claude Fable 5: Capabilities, Pricing ($10/$50), and When to Use It Over Opus 4.8

    Claude Fable 5: Capabilities, Pricing ($10/$50), and When to Use It Over Opus 4.8

    Anthropic released Claude Fable 5 on June 9, 2026 — and it’s the most capable model the company has ever made publicly available. After tracking every Claude release since the original 100K context window dropped, I can say this one is different. Fable 5 isn’t just an incremental update. It’s Anthropic’s Mythos-class model — the one they’d been keeping restricted — now opened up to anyone with an API key or a Claude subscription.

    Here’s what you need to know: the pricing, the benchmarks, and the specific decision framework for when to use Fable 5 versus sticking with Opus 4.8.

    Quick answer: Fable 5 costs $10/$50 per million input/output tokens (2x the cost of Opus 4.8). It outperforms Opus 4.8 significantly on complex coding, long-horizon tasks, and scientific research. Use Fable 5 when quality on hard problems justifies the cost. Use Opus 4.8 for high-volume, well-scoped, routine work.

    What Is Claude Fable 5?

    Claude Fable 5 (claude-fable-5) is Anthropic’s first publicly available Mythos-class model. The Mythos line is Anthropic’s highest capability tier — models that were previously restricted to research and select enterprise partners because of their raw power. Fable 5 is the version Anthropic deemed safe enough to release broadly.

    The name shift (from the Opus/Sonnet/Haiku tier naming) signals something intentional. Fable 5 sits above the Opus line entirely. It’s a new ceiling.

    Key specs:

    • Context window: 1M tokens (same as Opus 4.8)
    • Max output: 128K tokens per request
    • Thinking: Adaptive (always on — not a separate “thinking mode”)
    • Vision: Yes
    • Tool use / function calling: Yes
    • Available: Claude API, AWS Bedrock, Vertex AI, Microsoft Foundry

    Claude Fable 5 Pricing

    Model Input (per MTok) Output (per MTok) Context
    Claude Fable 5 $10.00 $50.00 1M tokens
    Claude Opus 4.8 $5.00 $25.00 1M tokens
    Claude Sonnet 4.6 $3.00 $15.00 1M tokens
    Claude Haiku 4.5 $1.00 $5.00 200K tokens

    Fable 5 costs exactly 2x Opus 4.8 on API. On subscription plans (Pro, Max, Team, Enterprise seat-based), Fable 5 is included at no extra cost through June 22, 2026.

    The free-until-June-22 window matters if you’re evaluating whether to route your workloads to Fable 5. Use that window to benchmark it against your actual tasks before the 2x cost kicks in.

    Benchmark Performance: Where Fable 5 Pulls Away

    The benchmarks that matter most are the ones that measure what the model can do on real engineering work, not trivia:

    Benchmark Claude Fable 5 Claude Opus 4.8 Delta
    SWE-bench Verified 95.0% 88.6% +6.4 pts
    SWE-bench Pro 80.0% 69.2% +10.8 pts
    FrontierCode 29.3% 13.4% ~2.2x
    Senior Engineer benchmark 91/100 ~63/100 +45% absolute

    The Senior Engineer benchmark is the one I find most telling. It’s designed to be hard for people who write code for a living — and Fable 5 scores 45 percentage points higher than Opus 4.8. That gap is significant enough that it changes the calculus for serious engineering work.

    When to Use Claude Fable 5 (vs Opus 4.8)

    I’ve been routing tasks between models for long enough to have a framework. Here’s how I think about it:

    Use Fable 5 when:

    • You’re running a large migration, refactor, or multi-stage software project
    • Quality on a hard problem matters more than per-token cost
    • You’re doing deep research, complex analysis, or long-horizon agentic work
    • The task would otherwise take a senior engineer half a day or more
    • You’re in the free evaluation window (through June 22) and want to benchmark

    Use Opus 4.8 when:

    • The task is well-scoped and routine
    • You’re running high-volume pipelines where 2x cost compounds fast
    • Latency matters — Fable 5 can take 60 seconds to several minutes on complex tasks vs 3–15 seconds for Opus 4.8
    • The task falls in Fable 5’s restricted domains (cybersecurity, biology, chemistry, distillation) — in those categories, Fable 5 routes to Opus 4.8 anyway, so you’d pay Fable 5 prices for Opus 4.8 output

    The smart routing strategy: Fable 5 for the hard jobs, Opus 4.8 for the rest. Don’t use Fable 5 as your default model — the cost and latency delta aren’t worth it for routine tasks.

    Important Limitations to Know Before You Switch

    Two limitations that don’t get enough coverage:

    1. Safety classifier routing. Fable 5 includes enhanced safety classifiers. For prompts touching cybersecurity, biology, chemistry, and distillation, those classifiers route the request to a Claude Opus 4.8 fallback. You pay Fable 5 API rates ($10/$50) but get Opus 4.8 output. If your use case is in these domains, Fable 5 is not the upgrade it appears to be.

    2. Data retention requirement. Fable 5 carries a mandatory 30-day data retention policy — Anthropic needs retained prompts and outputs to operate the safety classifiers. Claude Opus 4.8 is available under zero data retention (ZDR). If your use case requires ZDR (healthcare, legal, finance with strict data handling), stick with Opus 4.8 until Anthropic updates Fable 5’s data policy.

    Availability

    Claude Fable 5 is generally available as of June 9, 2026 on:

    • Claude API (claude-fable-5)
    • Claude Platform on AWS / Amazon Bedrock
    • Google Cloud Vertex AI
    • Microsoft Azure AI Foundry / GitHub Copilot

    Subscription access (free through June 22, 2026): Claude Pro ($20/mo), Max 5x ($100/mo), Max 20x ($200/mo), Team, and seat-based Enterprise plans all include Fable 5 access at no extra charge during the launch window. After June 22, the plan-tier access picture may change — check Anthropic’s pricing page for updates.

    How This Changes the Claude Model Decision Tree

    Before Fable 5, the Claude decision tree was straightforward:

    • Need the best? → Opus 4.8
    • Need balance? → Sonnet 4.6
    • Need speed/cost? → Haiku 4.5

    Now it’s:

    • Hard problems, complex projects, long-horizon work → Fable 5
    • Everyday work, high-volume pipelines → Opus 4.8
    • Balance of cost and capability → Sonnet 4.6
    • Speed and cost optimization → Haiku 4.5

    The introduction of a model tier above Opus 4.8 doesn’t replace the existing lineup — it creates a new ceiling for the work that genuinely needs it.

    Frequently Asked Questions

    Is Claude Fable 5 better than Opus 4.8?
    For complex coding, multi-stage tasks, and long-horizon work: yes, significantly. On SWE-bench Pro, Fable 5 scores 80.0% vs Opus 4.8’s 69.2% — a 10+ point gap. For routine, well-scoped tasks: the gap narrows enough that Opus 4.8’s 2x cost advantage makes it the smarter choice.

    What is the Claude Fable 5 API model ID?
    claude-fable-5. This is the API string you pass to model in your API calls.

    Does Fable 5 cost more than Opus 4.8?
    Yes — exactly 2x. Fable 5 is $10 input / $50 output per million tokens. Opus 4.8 is $5/$25. Through June 22, 2026, Fable 5 is included in Claude subscription plans at no extra cost.

    Can I use Claude Fable 5 for free?
    On Pro, Max, Team, and Enterprise subscription plans, yes — through June 22, 2026. API access is metered at $10/$50 per MTok from day one.

    Does Claude Fable 5 support zero data retention (ZDR)?
    No. Fable 5 carries a mandatory 30-day data retention requirement. If your use case requires ZDR, use Claude Opus 4.8, which supports it.

    What’s the difference between Claude Fable 5 and Claude Mythos 5?
    Mythos 5 is Anthropic’s fully restricted research model — not publicly available. Fable 5 is the Mythos-class model that Anthropic has prepared for general availability, with safety classifiers and the 30-day retention policy. You can think of Fable 5 as “Mythos for the real world.”

    Last verified: June 12, 2026. Anthropic pricing and availability subject to change — check Anthropic’s pricing page for current rates.