Tag: Anthropic

How to Set Up Claude Tag in Slack (and What to Lock Down First)
This is part of our Claude Tag field guide for agencies. Start with the overview: Claude Tag: A Builder’s Guide for Agencies.

Setting up Claude Tag in Slack takes a few minutes. The clicks are easy. The decisions you make while you click — who can reach it, which channels it sees, whether it’s proactive — are the part that actually matters. This is a security-first walkthrough: how to install it, and what to lock down before you do.

The install, in plain steps

The install, in plain steps.
1. Open the Install Claude for Slack link, which takes you to the Slack Marketplace listing.
2. Click Add to Slack and approve the requested permissions.
3. Choose the scope: the whole workspace (Anthropic’s recommended default) or a specific set of channels.
One important gotcha: only a Slack Primary Owner or Owner can set up Claude Tag’s access and channels. The Admin role can’t do this part. If you’re rolling it out for a team, make sure an Owner is the one configuring access — otherwise you’ll get halfway and stall.

Lock this down first: who can reach Claude

Lock down who can reach Claude first.

Claude Tag gives you three Member Access modes. Pick the tightest one that still lets the right people work:
- Anyone in the Slack workspace — broadest; fine for a single internal team, risky if outside collaborators or clients are guests in your workspace.
- Any member of your Claude organization — narrower; ties access to your Claude org, not just Slack presence.
- Role-based access — tightest; only members whose role allows it. This one is available on the Claude Enterprise plan.
Default to the narrowest mode that doesn’t block real work. You can always widen later; clawing access back after the fact is harder.

Then decide what Claude can see

Access is who can talk to Claude. Visibility is what Claude can read — and it’s the bigger lever. Two settings deserve a deliberate decision, not a default:
- Cross-channel learning is permission-gated — Claude only learns from other channels and data sources you allow, and it doesn’t report from private channels. Grant it per channel, and never let a channel holding one client’s (or one regulated dataset’s) data feed learning that other work can draw on.
- Ambient mode turns Claude proactive. Leave it off for anything client-facing or sensitive, and on only where all the data is yours. We break down that call in Claude Tag Ambient Mode: Useful Teammate or Context-Bleed Risk?
The lock-down-first checklist

The lock-down-first checklist.
1. Map channels to trust boundaries before you enable anything — mark each channel internal, client, or regulated.
2. Set Member Access to the narrowest mode that works.
3. Ambient mode OFF by default; on only for internal-only channels.
4. Cross-channel learning granted per channel, never from client/regulated channels.
5. Isolate client work in its own space, not just a channel in one shared brain — the reasoning is in The Multi-Client Isolation Trap.
6. Keep a human on the ship button for anything that leaves the building.
If you’re migrating from the old app

Claude Tag replaces the legacy Claude in Slack app. The old app switches over on August 3, 2026, and administrators have a 30-day window to opt in and control channel-level access. Don’t treat the migration as a silent upgrade — it’s the moment to redo these access and visibility decisions from scratch. More on what changed: Claude Tag vs. the Old Claude in Slack App.

For the exact, current setup screens, Anthropic keeps an admin setup guide in its documentation; the decisions above are what to bring to it. For the full field guide, start at the pillar: Claude Tag: A Builder’s Guide for Agencies.
June 23, 2026

Claude Ambient Mode & Background Tasks: Setup Guide

This is part of our Claude Tag field guide for agencies. Start with the overview: Claude Tag: A Builder’s Guide for Agencies.

Ambient mode is Claude Tag’s headline feature and its single most consequential setting. Turn it on and Claude stops waiting to be asked — it starts watching the channels it’s in and speaking up when it thinks you’d want to know something. Whether you should enable it isn’t a yes-or-no question. It’s a where question, and getting the where right is the whole game.

What ambient mode actually does

Four-step loop: observe, remember, act, update for managed agents — What ambient mode actually does.

By default, Claude Tag is reactive: you @-mention it, it works, it replies. With ambient behavior enabled, it becomes proactive. Anthropic describes it as Claude keeping you updated about whatever it thinks you might need to know — flagging relevant information from across the channels it’s in and the tools it’s connected to, and following up on threads or tasks that have gone quiet.

In practice that means three things: it surfaces context you didn’t ask for, it connects information across more than one channel, and it chases loose ends nobody assigned it. Those are exactly the behaviors that make it feel like a teammate instead of a tool.

Where it’s a superpower

Inside a single team, ambient mode is close to magic. Every channel belongs to the same company, so “learning across channels” only ever connects your own dots. A proactive teammate that remembers the forgotten follow-up, links the spec to the standup, and flags the blocker before it bites is pure upside. This is the version Anthropic runs internally, and it’s why they can say a large share of their product team’s code now comes from their own version of the tool.

If your Slack workspace is one company’s data and one team’s work, turn ambient mode on and enjoy it.

Where it’s a risk

Five security domains: identity, data, code governance, audit, agents — Where ambient mode is a risk.

Ambient mode’s proactive, cross-channel nature is exactly what makes it dangerous in two situations:

Multiple clients in one operation. The moment a proactive teammate is “surfacing relevant information from across channels,” relevance becomes the judge of what crosses the line between Client A and Client B. That’s a context-bleed risk we’ve lived — the whole subject of The Multi-Client Isolation Trap.
Regulated or sensitive data. Anywhere an unprompted message pulling context from elsewhere could expose something it shouldn’t — health, financial, legal, HR — proactive surfacing is a liability, not a convenience.

A simple decision framework

Desk with laptop, checklist notebook, and billing card ready before creating an Anthropic API key — A simple decision framework before you enable it.

Don’t decide ambient mode globally. Decide it per surface, with one question: is everything this Claude can see owned by the same trust boundary?

Surface	Ambient mode	Why
Internal team channels (one company)	ON	Cross-channel proactivity only connects your own data
Client-facing / multi-tenant channels	OFF	Proactive surfacing is where one client’s context leaks into another’s
Regulated / sensitive-data channels	OFF	Unprompted context-pulling is a compliance liability

The rule of thumb: ambient mode should be on where the data is all yours, and off everywhere a human should still be pulling, not the AI pushing.

If you do turn it on

Enable it deliberately, not by default. Map which channels hold which trust boundary before you flip the switch, keep client and regulated channels out of cross-channel learning, and audit what the assistant can actually see. That sequencing — boundaries first, then ambient — is exactly how we walk through it in How to Set Up Claude Tag in Slack.

The bottom line

Ambient mode isn’t good or bad — it’s powerful, and power needs a boundary. For internal teams, it’s the best part of Claude Tag. For client work, it’s the part to leave off until isolation is airtight. For the full picture, start at the pillar: Claude Tag: A Builder’s Guide for Agencies.

June 23, 2026

Claude Tag: A Builder’s Guide (2026)

Today Anthropic launched Claude Tag — a new way to work with Claude that starts inside Slack. Instead of a chatbot you visit, Claude joins your workspace as a teammate. You @-mention it with a request, it breaks the task into stages, works through them, and replies in the thread with what it made.

We read the announcement with a strange feeling, because we’d been running a version of this loop for client delivery for weeks. So this isn’t a reaction piece written from the outside. It’s a field guide from a team that built the same thing first — what Anthropic got right, what’s genuinely better in their version, and the one design choice that’s quietly dangerous if you run an agency.

What Claude Tag actually is

Side-by-side cards defining what Claude Code is and is not — What Claude Tag actually is.

A Slack-native teammate you delegate to by tagging @Claude — no separate app to open.
Multiplayer by default: one shared Claude per channel; anyone can see its work and pick up where the last person left off.
Context that compounds: it follows the channel over time, and with permission can learn from other channels and data sources.
Ambient mode: turn it on and Claude takes initiative — surfacing what’s relevant, flagging stale threads, following up on forgotten tasks.

It runs on Opus 4.8, replaces the older “Claude in Slack” app (admins opt in within 30 days), and is in beta for Enterprise and Team plans. Anthropic says 65% of their product team’s code now comes from their internal version. That number is the tell: this isn’t a toy.

What they got right

The unit of work is a request, not a conversation. “@Claude, draft the launch email and three follow-ups” is how people actually delegate.
Shared context beats private chats — auditable and collaborative; private AI sessions create shadow work nobody can review.
It meets people where the work already is. The work happens in Slack, so the AI lives in Slack.

The one thing agencies have to get right (and Claude Tag doesn’t, by default)

Claude Tag’s standout features — ambient mode and cross-channel learning — are wonderful when every channel belongs to one company. But an agency is many clients sharing one operation. The moment your AI teammate “learns across channels and data sources,” context from Client A can surface in work for Client B.

We learned this by living it. In an early pilot, a single shared context produced client deliverables that pulled in details from the wrong account. Nothing left the building, but the signal was clear: for client work, ambient cross-channel learning is not a feature — it’s a breach waiting for a deadline.

So we rebuilt around two non-negotiables:

Hard isolation per client — each client’s room is walled, enforced in the architecture, not a prompt you hope it obeys.
Approve-before-ship — the AI drafts; a human reviews; only then does it go out.

If you take one thing from this guide: the two things that make Claude Tag magical inside a company are the two things you must switch off — or wall off — to use it safely for clients.

The pattern that works: split by surface

Surface	Use	Why
Your internal team	Adopt Claude Tag	Ambient cross-channel learning is a feature when all the data is yours
Client-facing delivery	Isolated room + approval gate	Isolation and human sign-off are the product

How to roll it out without getting burned

Map channels by trust boundary; client-data channels don’t get cross-channel learning.
Default ambient mode OFF for anything client-facing.
Keep humans on the ship button for anything that leaves the building.
Audit what the AI can see — your permission is the control; set it deliberately.
Separate client work into isolated spaces, not just channels in one shared brain.

Where this goes

Claude Tag is a milestone: the AI teammate is now an operating model, not a demo. For internal teams, adopt it. For client work, the hard, valuable part — isolation, trust, a human in the loop — is still yours to own. That’s what we build for clients at Tygart Media.

The rest of the field guide

This pillar is the overview. The cluster goes deeper:

We Built a Slack AI Teammate Before Claude Tag — the origin story, and the exact loop we ran before the launch.
Claude Tag for Agencies: The Multi-Client Isolation Trap — why ambient cross-channel learning breaks for client work, and the architecture that fixes it.
Claude Tag vs. the Old Claude in Slack App: What Changed — the migration, the August 3 cutoff, and what to check first.
Claude Tag Ambient Mode: Useful Teammate or Context-Bleed Risk? — where proactive mode is a superpower and where to leave it off.
How to Set Up Claude Tag in Slack (and What to Lock Down First) — the security-first setup checklist.
Claude Tag Pricing: Enterprise vs Team, and When Self-Hosting Wins — the build-vs-buy calculus for agencies.

June 23, 2026

Claude Tag vs. the Old Claude in Slack App: What Changed
This is part of our Claude Tag field guide for agencies. Start with the overview: Claude Tag: A Builder’s Guide for Agencies.

If your team already used the “Claude in Slack” app, Claude Tag is not an add-on — it’s the replacement. Anthropic has said Claude Tag replaces the existing Claude in Slack app, administrators have a 30-day window to opt in, and the legacy app is retired on August 3. So this isn’t a “should we try it” decision. It’s a migration with a clock on it. Here’s what actually changed, and what to check before you flip the switch.

What’s genuinely new

What is genuinely new in Claude Tag.

The old integration was, in practice, a way to summon Claude in a thread. Claude Tag changes the model from “a chatbot you call” to “a teammate that stays.” Four things are new:
- Multiplayer per channel. Within a given Slack channel, there’s one Claude that interacts with everyone. Anyone can tag it in and pick up where the last person left off, instead of each person holding a private session.
- Ambient mode. When enabled, Claude proactively keeps people updated about what it thinks they need to know — flagging relevant information, following up on forgotten threads — rather than waiting to be asked.
- Cross-channel learning. With permission, Claude can learn from other Slack channels and data sources. (Anthropic notes it doesn’t report from private channels.)
- Opus 4.8 underneath. Claude Tag runs on Opus 4.8, so the reasoning behind the delegation is the current-generation model, not whatever the old app was pinned to.
The migration timeline, plainly

Migration timeline, plainly.

Three dates and facts matter:
1. Claude Tag is available today in beta for Claude Enterprise and Team customers.
2. Administrators have 30 days to opt in and migrate.
3. The old Claude in Slack app is retired on August 3. If you do nothing, that capability goes away.
Anthropic is also issuing an introductory launch credit to eligible Enterprise and Team organizations, which makes the trial period genuinely low-stakes for internal use.

What to check before you switch — especially if you serve clients

What to check before you switch — especially for clients.

For a single-company team, migrating is close to a no-brainer: you get a better model and a more capable teammate, and the launch credit covers the experiment. If you’re an agency or anyone handling more than one client’s data in one workspace, three checks come first:
1. Decide cross-channel learning per channel, not globally. The new superpower is also the new risk. A channel that holds one client’s data should never feed learning that another client’s work can draw on. Map your channels to trust boundaries before you grant any cross-channel permission.
2. Default ambient mode OFF for client-facing channels. Proactive surfacing is wonderful internally and dangerous across tenants. Turn it on where the data is all yours; leave it off where it isn’t.
3. Keep your approval gate. Whatever human sign-off you had on outbound work in the old setup, carry it forward. A more autonomous teammate raises the stakes on “who hits send.”
Our take

Adopt it internally now — the model upgrade and the multiplayer surface are worth it, and the clock makes the decision for you anyway. For client delivery, migrate deliberately: the same features that make Claude Tag better make isolation harder, and isolation is the thing you can’t get wrong. We unpack exactly that failure mode in The Multi-Client Isolation Trap, and the on/off call for proactive behavior in Claude Tag Ambient Mode.

For the full picture, start at the pillar: Claude Tag: A Builder’s Guide for Agencies.
June 23, 2026

Claude Tag for Agencies: The Multi-Client Isolation Trap

This is part of our Claude Tag field guide for agencies. Start with the overview: Claude Tag: A Builder’s Guide for Agencies.

Claude Tag’s two best features are ambient mode and cross-channel learning. Inside a single company, they are close to magic: one AI teammate that quietly learns how the whole organization works and surfaces the right thing at the right moment. If you run an agency, those same two features are a trap. This piece is about why, and exactly what to build instead.

Why an agency is a different shape of problem

A company is one tenant. Every channel, every document, every thread belongs to the same entity, so an AI that “learns across channels and data sources” is only ever connecting your own dots. That is the design Claude Tag is optimized for, and Anthropic’s own number — 65% of their product team’s code now comes from their internal version — shows how well it works when all the data is yours.

An agency is the opposite shape. You are many clients sharing one operation. Client A and Client B may be competitors. The instant your AI teammate is allowed to learn across channels, the wall between those two accounts depends on the model’s judgment about what is “relevant” — and relevance is exactly the thing it’s designed to be generous about. Cross-channel learning isn’t a bug here. It’s a feature pointed in the wrong direction.

The lesson we learned by living it

We didn’t reason our way to this. We hit it. In an early pilot, running a single shared context across more than one account, the assistant produced a client deliverable that pulled in details from the wrong account. Nothing left the building — the human review caught it — but the signal was unmistakable. For client work, ambient cross-channel learning is not a feature. It’s a breach waiting for a deadline, because the day it slips through is the day someone is moving too fast to catch it.

That single near-miss reorganized how we build. It is the reason we treat isolation as architecture, not etiquette.

Why “don’t mix clients” in a prompt is not a control

The tempting fix is to tell the assistant, in its instructions, to keep clients separate. Don’t rely on it. A prompt is a request for good behavior; it is not a boundary. Under deadline pressure, with a helpful model trying to surface everything relevant, “please don’t cross the streams” is the first thing to bend. Isolation that matters is enforced in the structure of the system — in what the assistant can even see — not in what you politely ask it not to do.

The pattern that works: split by surface

Three stacked layers: chat UI, tools, agent runtime — Split by surface — the isolation pattern that works.

The move that resolved it for us was to stop treating “internal” and “client-facing” as the same problem. They get different architectures:

Surface	Use	Why
Your internal team	Adopt Claude Tag fully	Ambient mode and cross-channel learning are features when all the data is yours
Client-facing delivery	Isolated room + approval gate	Per-client isolation and human sign-off are the product, not overhead on it

Internally, turn everything on. Let it learn across your channels, run ambient, follow up on your forgotten threads. For client work, each client gets a walled room that cannot see any other client’s context, and nothing leaves that room without a human approving it.

Do this instead: a concrete checklist

One isolated space per client — not one shared brain with channels. The boundary should be the space itself, enforced by what data the assistant is connected to, so there is nothing to “accidentally” pull from another account.
Cross-channel learning OFF for anything client-facing. It is the single setting most likely to cause a bleed. Reserve it for internal-only surfaces.
Ambient mode OFF on client rooms by default. Proactive surfacing is where unrequested context shows up. Let humans pull in a client room; let the AI push only where the data is all yours.
A human on the ship button for everything that leaves the building. The AI drafts; a person reviews and approves; only then does it go to the client. This is the control that caught our near-miss.
Audit what the assistant can see, deliberately. Permissions are the real boundary. Set them on purpose, write them down, and review them when you add a client.
Map every channel to a trust boundary before you turn anything on. Decide, per channel, whether it is internal or client data — and never let a client-data channel feed cross-channel learning.

The one sentence to take with you

The two things that make Claude Tag magical inside a company — ambient mode and cross-channel learning — are the two things you must wall off to use it safely for clients. Get that right and you get the upside without betting the client relationship on a model’s judgment about relevance.

For the origin story of how we built this loop before the launch, read We Built a Slack AI Teammate Before Claude Tag. For the full guide, start at the pillar: Claude Tag: A Builder’s Guide for Agencies. This is the kind of isolation-and-approval architecture we build for clients at Tygart Media.

June 23, 2026

We Built a Slack AI Teammate Before Claude Tag
This is part of our Claude Tag field guide for agencies. Start with the overview: Claude Tag: A Builder’s Guide for Agencies.

The night before Anthropic launched Claude Tag, we shipped two client deliverables through a Slack-based AI teammate we had built ourselves. We weren’t racing anyone and we had no idea an announcement was coming the next morning. We were just doing the work the way we’d been doing it for weeks: post a request in a channel, let Claude draft, approve it, and let it go out.

So when Anthropic described Claude Tag — tag @Claude with a request, and it breaks the task into stages and works through them in the thread — we recognized it on sight. This is the build log of the version we made first: what it is, why we put it in Slack, and the one piece we deliberately kept under human control.

Why we were building an AI teammate in Slack at all

Why we were building an AI teammate in Slack.

We didn’t set out to build an “AI tool.” We set out to close the gap between a decision and the thing the decision produces. A lead comes in and someone says “we should send the follow-up sequence today.” A week ends and someone says “the client update needs to go out.” The decision is made in seconds; the production used to take an hour. That hour is where work stalls.

Slack was the obvious surface because that is where the deciding already happens. We didn’t want a separate dashboard nobody opens, or a chatbot in another tab that creates a second copy of the conversation. We wanted the request and the result to live in the same thread, where anyone on the team can see both. Putting the AI where the work already is turned out to be most of the design.

The loop, stage by stage

The loop, stage by stage.

The whole system is one loop with four moves:
1. Request. Someone posts a plain-language ask in a channel — “draft the new-lead follow-up sequence,” “write this week’s update post.” No special syntax, no form.
2. Draft. The teammate picks it up, breaks it into stages, and produces the actual deliverable in the thread — not a summary of what it would do, the thing itself.
3. Claim and approve. A human takes the draft, reads it, edits if needed, and signs off. Nothing moves on the AI’s say-so alone.
4. Ship. On approval, the deliverable goes to its real destination — the CRM, the CMS, the inbox — and the thread records that it happened.
The night we ran it end to end, twice, the part that struck us wasn’t the drafting. It was how natural the “claim and approve” step felt. Delegating to the teammate looked exactly like delegating to a person: ask in the channel, get a draft back, give it a yes.

The runner that holds no keys

The piece we’re proudest of is invisible in the thread. The process that reads the queue and carries out approved work does not carry standing credentials. The keys to the CRM, the publishing platform, the email system — none of them live inside the bot. They sit in the platform’s secret store and are handed to the action at the moment it runs, scoped to that job.

This sounds like plumbing, but for an agency it is the difference between safe and reckless. The component most exposed to the outside world — the thing listening to a chat channel — is the component holding the least. If that surface were ever compromised, there is no client’s API key sitting in it to steal. We built it that way before it was convenient, because client trust is the entire business.

What surprised us
- A request is a better unit than a conversation. “Draft the launch email and three follow-ups” is how people actually delegate. Framing the work as a request instead of a chat changed how the team used it — less hand-holding, more handing-off.
- Visible beats private. Because the work happened in a shared channel, anyone could see what was asked and what came back. Private AI sessions create shadow work nobody can review. Doing it in the open made it auditable by default.
- The approval step wasn’t a bottleneck. It was the product. We expected the human sign-off to feel like friction. Instead it was the thing that let us trust the output enough to send it to a client at all.
What Claude Tag changes for us

What Claude Tag changes for us.

Anthropic just productized the surface we’d been hand-building: a Slack-native teammate, multiplayer per channel, with an ambient mode and cross-channel learning, running on Opus 4.8. For our internal team, that’s a gift — we can adopt it and retire some of our own scaffolding.

For client delivery, the hard and valuable part is still ours to own: keeping each client’s context walled off from every other, and keeping a human on the ship button. Those two things are exactly what Claude Tag’s best features work against by default — which is the whole subject of the next piece: Claude Tag for Agencies: The Multi-Client Isolation Trap. For the full picture, go back to the pillar: Claude Tag: A Builder’s Guide for Agencies.
June 23, 2026

Claude Fable 5 Pricing and Access (2026)

Last verified: August 26, 2026 (Pacific Time)

Direct Answer (August 2026): Claude Fable 5 is positioned as Anthropic’s advanced research & multi-agent architecture model. On API endpoints, Fable 5 is priced at $10.00/MTok input ($1.00 cached read) and $50.00/MTok output, available for specialized agentic reasoning workloads.

Claude Fable 5 (claude-fable-5) is Anthropic’s most capable widely released model, built for the most demanding reasoning and long-horizon agentic work. On the Claude API it is priced at $10 per million input tokens and $50 per million output tokens — double the rate of Claude Opus 4.8 — with a 1M-token context window and up to 128K output tokens per request. It reached general availability on June 9, 2026. The verified pricing and access details are below.

Pricing at a glance

Infographic ladder of Claude plans: Free, Pro, Max, Team, and Enterprise — Fable 5 pricing at a glance — stale-proof framing.

All figures below are from Anthropic’s official pricing and models pages. Prices are in USD per million tokens (MTok). Fable 5 includes the full 1M-token context window at standard pricing — there is no long-context premium.

Item	Claude Fable 5
Model ID (API)	`claude-fable-5`
Base input	$10 / MTok
Output	$50 / MTok
5-minute cache write	$12.50 / MTok
1-hour cache write	$20 / MTok
Cache hit / read	$1 / MTok
Batch API input / output	$5 / MTok · $25 / MTok
Context window	1M tokens
Max output	128K tokens

How Fable 5 compares to Opus, Sonnet, and Haiku

Pyramid diagram of Claude tiers: fast volume base, production workhorse middle, deep flagship peak — How Fable 5 compares to Opus, Sonnet, and Haiku.

Fable 5 sits at the top of Anthropic’s lineup, a tier above the Opus models. The per-token cost difference is the clearest way to see where it fits.

Model	Input $/MTok	Output $/MTok	Context	Max output
Claude Fable 5	$10	$50	1M	128K
Claude Opus 4.8	$5	$25	1M	128K
Claude Sonnet 4.6	$3	$15	1M	64K
Claude Haiku 4.5	$1	$5	200K	64K

Where you can use Fable 5

Three cards for fast volume, daily workhorse, and deep flagship Claude seats — Where you can use Fable 5.

At general availability, Fable 5 is offered across Anthropic’s first-party API and all major cloud platforms, plus claude.ai subscription plans (subject to the access note below). The model IDs differ by platform.

Surface	Availability / model ID
Claude API (first-party)	Generally available — `claude-fable-5`
Claude Platform on AWS	Generally available — `claude-fable-5`
Amazon Bedrock	Generally available — `anthropic.claude-fable-5`
Google Vertex AI	Generally available — `claude-fable-5`
Microsoft Foundry	Generally available
claude.ai — Pro, Max, Team, Enterprise	Promotional access June 9–22, 2026 (see below)
claude.ai — Free plan	Not included

Consumer-plan access and the promotional window

For claude.ai subscribers, Anthropic launched Fable 5 with a time-limited promotion rather than a permanent plan inclusion. From June 9 through June 22, 2026, Fable 5 was included on the Pro, Max, Team, and seat-based Enterprise plans at no extra charge. During that window, Anthropic’s documentation states that Fable 5 usage “counts toward your plan’s usage limits, and you won’t be charged anything extra,” but that it draws from those limits “at a higher rate than other models.” The Free plan was explicitly excluded.

Anthropic’s announced plan was that after June 22, 2026, Fable 5 would no longer be included in plan usage limits, and continued use on claude.ai would require usage credits — a pay-as-you-go balance for usage beyond what a plan includes.

Integration notes that affect cost and handling

Fable 5 differs from the Opus, Sonnet, and Haiku models in a few ways that matter when you wire it into an application. It ships with safety classifiers that can decline a request: when that happens, the Messages API returns stop_reason: "refusal" as a successful HTTP 200 response, not an error. You are not billed for a request that is refused before any output is generated, and Anthropic provides server-side, client-side, and manual fallback paths to retry on another Claude model. Adaptive thinking is always on (thinking: {"type": "disabled"} is not supported), and the raw chain of thought is never returned — thinking.display controls whether thinking blocks contain a summary or are empty. Fable 5 also uses the tokenizer introduced with Opus 4.7, which can produce roughly 30–35% more tokens for the same text than older models, so re-baseline your token counts rather than assuming parity with earlier Claude models.

How much does Claude Fable 5 cost?

On the Claude API, Fable 5 costs $10 per million input tokens and $50 per million output tokens. Prompt-cache writes are $12.50/MTok (5-minute) or $20/MTok (1-hour), cache reads are $1/MTok, and the Batch API halves the rate to $5/MTok input and $25/MTok output.

Is Fable 5 more expensive than Claude Opus 4.8?

Yes. Fable 5 is priced at exactly double Opus 4.8 on both input ($10 vs $5 per MTok) and output ($50 vs $25 per MTok). Both share a 1M-token context window and 128K max output.

Which claude.ai plans include Fable 5?

From June 9 to June 22, 2026, Fable 5 was included on the Pro, Max, Team, and seat-based Enterprise plans at no extra cost, drawing from plan usage limits at a higher rate. The Free plan was not included. Anthropic’s plan was to move continued claude.ai use to usage credits after June 22.

What is the difference between Fable 5 and Mythos 5?

They share the same specs ($10/$50 per MTok, 1M context, 128K output) and June 9, 2026 launch date. Fable 5 is the generally available model with built-in safety classifiers that can decline requests; Mythos 5 is offered only in limited availability.

💼 Deploying Claude or AI Infrastructure in Your Business?

At Tygart Media, we engineer custom Model Context Protocol (MCP) servers, multi-model content pipelines, and AI operational systems. Explore our Claude AI Team Implementation Services or check out our complete Restoration Operations & AI Kit.

June 13, 2026

Claude Message Batches API: 50% Pricing, Limit (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 â€” 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

Workshop fuel gauge and metal tokens pouring into an API hopper, metaphor for pay-per-token pricing — Batch pricing — half-rate framing without sticky dollars.

Every token processed through the Message Batches API is billed at half the standard input and output price. No quality difference from synchronous requests â€” 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

Infographic with three panels: protect the service, fair share, and cost control explaining rate limits — Key limits at a glance — stale-proof.

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 â€” 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 â€” 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 â€” 1h TTL (batch)	2x Ã— 0.5x = 1x	$5.00	2x write cost, then 50% batch
Cache read (batch)	0.1x Ã— 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 â€” 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.

Related on Tygart Media: tokens to words · Claude Code billing · how much Claude costs.

June 13, 2026

How Many Words Is a Million Claude Tokens? (2026) â€” a (2026)

Last verified: June 13, 2026

A million Claude tokens equals roughly 750,000 words on Claude Sonnet 4.6 â€” 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)

Small token cubes assembling into short phrase cards on a desk — Token-to-word conversion framing.

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 â€” and why it matters

Diagram comparing a long context window bar with a shorter output limit bar — 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 â€” 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 â€” 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 â€” a 1M-token window filled with source code or dense structured data will span a very different page count.

Related on Tygart Media: Message Batches API · Claude pricing · Anthropic API quickstart.

June 13, 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 â€” 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

Three cards: coding depth, latency first, agent reliability — Cowork vs Code vs Agent SDK vs Managed Agents 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) â€” research, documents, file and spreadsheet work	Interactive, supervised â€” 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) â€” draws from your plan’s usage allocation
Claude Code	Developers doing interactive coding â€” build features, fix bugs, automate dev tasks	Interactive â€” 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 â€” 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 â€” 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 â€” 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 â€” 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.

Related on Tygart Media: what Claude Cowork is · Claude Code getting started · Agent SDK migration.

June 13, 2026

Tag: Anthropic

The install, in plain steps

Lock this down first: who can reach Claude

Then decide what Claude can see

The lock-down-first checklist

If you’re migrating from the old app

What ambient mode actually does

Where it’s a superpower

Where it’s a risk

A simple decision framework

If you do turn it on

The bottom line

What Claude Tag actually is

What they got right

The one thing agencies have to get right (and Claude Tag doesn’t, by default)

The pattern that works: split by surface

How to roll it out without getting burned

Where this goes

The rest of the field guide

What’s genuinely new

The migration timeline, plainly

What to check before you switch — especially if you serve clients

Our take

Why an agency is a different shape of problem

The lesson we learned by living it

Why “don’t mix clients” in a prompt is not a control

The pattern that works: split by surface

Do this instead: a concrete checklist

The one sentence to take with you

Why we were building an AI teammate in Slack at all

The loop, stage by stage

The runner that holds no keys

What surprised us

What Claude Tag changes for us

Pricing at a glance

How Fable 5 compares to Opus, Sonnet, and Haiku

Where you can use Fable 5

Consumer-plan access and the promotional window

Integration notes that affect cost and handling

How much does Claude Fable 5 cost?

Is Fable 5 more expensive than Claude Opus 4.8?

Which claude.ai plans include Fable 5?

What is the difference between Fable 5 and Mythos 5?

Pricing: 50% off standard rates

Key limits at a glance

Message Batches API rate limits by tier

Stacking batch pricing with prompt caching

How results come back

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

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

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

How long are batch results available for download?

Is the Batches API eligible for Zero Data Retention?

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

What the new tokenizer changed â€” and why it matters

Use count_tokens â€” not tiktoken or ratio math

Quick reference: common document sizes

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

Does Sonnet 4.6 use the new tokenizer?

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

Does the new tokenizer affect pricing?

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

The decision matrix

Where billing actually differs

SDK vs Managed Agents: the programmatic split

Quick chooser

Is Claude Cowork the same as Claude Code?

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

Where does the work actually execute for each surface?

Is the Agent SDK built on Claude Code?

Is Managed Agents generally available?