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AI123AI ModelsanthropicClaude Fable 5.1
Claude Fable 5.1

Claude Fable 5.1

anthropic

Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual code generation, and finance and analysis tasks in particular. It also tends to be more concise than Fable 5 in its plans and summaries. We recommend testing it as a direct upgrade wherever you use Fable 5 today, and alongside Opus 5 on reasoning-heavy tasks.

Modalities
TT
Input / output price
$10/$50per 1M tokens
Context
1.0M
Released
2026-09-01
PricingArtificial AnalysisDesign ArenaRelated Models

Pricing

Usage prices in USD. Token rates are per one million tokens; other billing units are shown with each charge.
InputOutputCache readCache writeCache write (1H)Web search
$10.00/ 1M tokens$50.00/ 1M tokens$0.25/ 1M tokens$12.50/ 1M tokens$20.00/ 1M tokens$0.01/ request

Artificial Analysis

Explore rankings
53.4

Intelligence index

Outperforms 99% of compared models

81.6

Coding index

Outperforms 100% of compared models

57.9

Agentic index

Outperforms 100% of compared models

Design Arena

Elo scores and win rates across design and agent tasks.

Category performance

1,000-1,500 Elo
3D: 1390; Asciiart: 1321; Code Categories: 1335; Data Visualization: 1344; Game Development: 1381; SVG: 1296; UI Component: 1333; Website: 13123D1390 EloAsciiart1321 EloCode Categories1335 EloData Visualization1344 EloGame Development1381 EloSVG1296 EloUI Component1333 EloWebsite1312 Elo

Model Arena

Asciiart

1321Elo

Win rate 65%

Data Visualization

1344Elo

Win rate 61.8%

Game Development

1381Elo

Win rate 65.6%

3D

1390Elo

Win rate 67.7%

Code Categories

1335Elo

Win rate 59.3%

SVG

1296Elo

Win rate 56.4%

Website

1312Elo

Win rate 56.3%

UI Component

1333Elo

Win rate 59.6%

Agent Arena

Fullstack

1326Elo

Win rate 57.2%

Godotgamedev

1327Elo

Win rate 60.7%

Htmlslides

1394Elo

Win rate 56.5%

Python-pptxslides

1318Elo

Win rate 60.8%

Spreadsheet

1416Elo

Win rate 70.4%

Webapps

1318Elo

Win rate 56.1%

Mobile Apps

1265Elo

Win rate 56.3%

How Design Arena works

Related Models

Claude Opus 5.5

Claude Opus 5.5

anthropic

Claude Opus 5.5 is Anthropic's flagship model for demanding reasoning, coding, and long-horizon agentic work, succeeding Claude Opus 5. It is particularly strong at multi-step changes in large codebases, code review and bug finding, financial and scientific analysis, and reading dense charts, diagrams, and screenshots, and it is more careful than its predecessor about only stating figures and citing sources it can back up. The model completes comparable tasks in fewer steps and with fewer tokens than Opus 5, and reports on its work in plainer language, with clear updates on what it did, what it found, and what it needs from the user. Thinking is always adaptive, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings remain effective for latency-sensitive workloads.

Context
1.0M
Max output
128K
Released
2026-09-22
Input
$4/M
Output
$20/M
Cache read
$0.2/M
Claude Sonnet 5.5

Claude Sonnet 5.5

anthropic

Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade. It is especially strong at building features, fixing bugs, and producing polished documents, slides, and spreadsheets, and it writes and communicates more clearly than its predecessor. Thinking is always on, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings keep it responsive for everyday agentic loops.

Context
1.0M
Max output
128K
Released
2026-09-28
Input
$2/M
Output
$10/M
Cache read
$0.1/M
Claude Opus 5

Claude Opus 5

anthropic

Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It is particularly strong at end-to-end software tasks, code review and bug finding, visual analysis of charts and documents, complex office deliverables, and coordinating parallel subagents. The model maintains strong instruction following and tool use across extended tasks, while remaining effective at lower effort settings for workloads that prioritize latency and token efficiency.

Context
1.0M
Max output
128K
Released
2026-07-24
Input
$5/M
Output
$25/M
Cache read
$0.5/M
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