GPAI Ledger › Model catalog (developer docs) (Anthropic) › Capture 26 Aug 2026

Model catalog (developer docs) — capture 20260826T083026Z

Verify: sha256sum 63b407c14130d54b176bac2ddada9f819885d242498cbc9d741137ba1448fdd8.html.txt must equal the hash above (the filename IS the expected hash); ots verify 63b407c14130d54b176bac2ddada9f819885d242498cbc9d741137ba1448fdd8.html.20260826T083026Z.ots -f 63b407c14130d54b176bac2ddada9f819885d242498cbc9d741137ba1448fdd8.html.txt (opentimestamps.org) proves the bytes existed no later than the attestation time — an upper bound on the capture time; the fetch time above is the archive's own record (a freshly captured proof reports 'pending' here: the calendars anchor within hours, but this archive only upgrades the stored proof to its anchor on a later run, so expect a day or two). ots verify needs a local Bitcoin Core node (a pruned one is fine); without one, ots info on the proof prints the attesting block height and merkle path to check on any block explorer.

Extracted text

Machine-extracted text (layout may be lost; the authoritative content is the stored file above).

Models overview - Claude Platform Docs
Claude Platform Docs
Messages
Managed Agents
Admin
Resources

Best practices
Models & pricing

CLI, SDKs, and libraries
Claude API skill
Release notes

API reference

English


Console
Log in


Search
Ctrl
K
Models
Models overview
Claude Fable 5
Claude Opus 5
Claude Sonnet 5
Claude Haiku 4.5
Specialized models

Legacy models

Guides
Choosing a model
Optimizing for cost and intelligence
Upgrade between model versions
Lifecycle and reference
Model IDs and versioning
Model deprecations
Model cards
Pricing
System prompts

Console
Claude Platform Docs


Solutions
AI agents
Code modernization
Coding
Customer support
Financial services
Government
Higher education
K-12 teachers
Life sciences
Partners
Claude on AWS
Claude on Google Cloud
Learn
Blog
Courses
Use cases
Connectors
Customer stories
Engineering at Anthropic
Events
Powered by Claude
Service partners
Startups program
Company
Anthropic
Careers
Economic Futures
Research
News
Responsible Scaling Policy
Security and compliance
Transparency
Learn
Blog
Courses
Use cases
Connectors
Customer stories
Engineering at Anthropic
Events
Powered by Claude
Service partners
Startups program
Help and security
Availability
Status
Support
Discord
Terms and policies
Privacy policy
Responsible disclosure policy
Terms of service: Commercial
Terms of service: Consumer
Usage policy

Models & pricing

Models
Models overview
Claude is a family of state-of-the-art large language models developed by Anthropic. Compare the current lineup, find the model ID for every platform, and open each model's page for its full specs and resources.

Copy page


Choosing a model

Pricing

Migration guide

Copy page



Compare models

If you're unsure which model to use, start with
Claude Opus 5
 for complex agentic coding and enterprise work; for the highest available capability, use
Claude Fable 5
. All current models support text and image input, text output, multilingual capabilities, vision, and tool use; each model’s page lists the platforms it is available on.

Feature
Claude Fable 5
Next-generation intelligence for long-running agents
Claude Opus 5
For complex agentic coding and enterprise work
Claude Sonnet 5
The best combination of speed and intelligence
Claude Haiku 4.5
The fastest model with near-frontier intelligence
Comparative latency

Slower
Moderate
Fast
Fastest
Pricing

$10 / input MTok
$50 / output MTok
$5 / input MTok
$25 / output MTok
$2 / input MTok
$10 / output MTok
$1 / input MTok
$5 / output MTok
Claude API ID

claude-fable-5
claude-opus-5
claude-sonnet-5
claude-haiku-4-5-20251001
Capabilities
Thinking

Adaptive (always on)
Adaptive
Adaptive
Extended
Default effort

high
high
high
Not supported
Context window

1M tokens
1M tokens
1M tokens
200K tokens
Max output

128K tokens
128K tokens
128K tokens
64K tokens
Reliable knowledge cutoff

Jan 2026
May 2026
Jan 2026
Feb 2025
Show all details
Show fewer details


Once you've picked a model,
learn how to make your first API call
. For how model IDs, aliases, and snapshots work, see
Model IDs and versioning
; for the reliable-knowledge and training-data cutoffs behind each model, see
Anthropic's Transparency Hub
.


Using the Models API

You can query model capabilities and token limits programmatically with the
Models API
. The response includes
max_input_tokens
,
max_tokens
, and a
capabilities
 object for every available model.


Prompt and output performance

Current Claude models excel in:

Performance:
 Top-tier results in reasoning, coding, multilingual tasks, long-context handling, honesty, and image processing. See
Prompting best practices
 for general and model-specific prompting guidance.

Engaging responses:
 Claude models are ideal for applications that require rich, human-like interactions. If you prefer more concise responses, adjust your prompts to guide the model toward the desired output length. Refer to the
prompt engineering guides
 for details.

Output quality:
 When migrating from a previous model generation, you may notice larger improvements in overall performance. If you're on Claude Opus 4.8 or earlier, see
Migrating to Claude Opus 5
.


Get started with Claude

If you're ready to start exploring what Claude can do for you, dive in! Whether you're a developer looking to integrate Claude into your applications or a user wanting to experience the power of AI firsthand, the following resources can help.


Intro to Claude
Explore Claude's capabilities and development flow.

Quickstart
Learn how to make your first API call in minutes.
Choosing a model
Establish criteria and pick the right model for your use case.
Pricing
Complete pricing, including batch discounts and prompt caching rates.

Model deprecations
Lifecycle status and retirement commitments for every model.

Claude Console
Craft and test prompts directly in your browser.

Looking to chat with Claude? Visit
claude.ai
. If you have questions, reach out to the
support team
 or the
Discord community
.
Was this page helpful?

