Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) vs. Claude Sonnet 5 (Adaptive Reasoning, High Effort)

Name: Intelligenz
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Vergleich von Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) und Claude Sonnet 5 (Adaptive Reasoning, High Effort) nach Intelligenz, Preis, Geschwindigkeit, Kontextfenster und weiteren Merkmalen.

Einzelheiten zu unserer Methodik finden Sie auf unserer Methodikseite.

	Anthropic	Anthropic
Intelligenz
Intelligence Index	53	32
AA-Briefcase	1662	1178
GDPval-AA v2	1764	1308
AutomationBench-AA	59%	32%
Terminal-Bench v4.0	52%	5%
SciCode	63%	54%
Humanity's Last Exam	59%	36%
GDP.pdf	26%	9%
CritPt	30%	15%
AA-Omniscience	43	−4
AA-LCR v1.1	85%	77%
Kosten
Preis pro 1 Mio. Tokens	$7.175	$1.54
Eingabepreis pro 1 Mio. Tokens	$10.00	$2.00
Ausgabepreis pro 1 Mio. Tokens	$50.00	$10.00
Cache-Hit-Preis pro 1 Mio. Tokens	$0.25	$0.20
Kosten pro Aufgabe	$7.63	$1.79
Kosten für den Intelligence Index	13.129 $	2.086 $
Tokenverbrauch
Ausgabe-Tokens pro Aufgabe	78k	44k
Reasoning-Tokens pro Aufgabe	47k	28k
Ausgabe-Tokens für den Intelligence Index	188M	89M
Leistung
Ausgabegeschwindigkeit	67 Tokens/s	60 Tokens/s
Zeit bis zum ersten Token	299.76 s	10.02 s
Zeit bis zum ersten Antwort-Token	299.76 s	10.02 s
Ende-zu-Ende-Antwortzeit	307.26 s	18.42 s
Zeit pro Aufgabe	743.85 s	434.10 s
Technische Spezifikationen
Kontextfenster	1000k Tokens~1.500 A4-Seiten in Arial mit Schriftgröße 12	1000k Tokens~1.500 A4-Seiten in Arial mit Schriftgröße 12
Veröffentlichungsdatum	September 2026	Juni 2026
Reasoning	Ja	Ja
Eingabemodalität	Unterstützt: Text und Bilder	Unterstützt: Text und Bilder
Ausgabemodalität	Unterstützt: Text	Unterstützt: Text
Offene Gewichte	Nein	Nein

Wichtigste Ergebnisse

Updated

Intelligenz

Artificial Analysis Intelligence Index · Higher is better

Geschwindigkeit

Output tokens per second · Higher is better

Kosten pro Aufgabe

Weighted average cost (USD) per Intelligence Index task · Lower is better

IntelligenzUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench v4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

𝜏³-Banking

Agentic tool use

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Vergleiche des Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Tokenverbrauch

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Kosten

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Kontextfenster

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Geschwindigkeit

Gemessen anhand der Ausgabegeschwindigkeit (Tokens pro Sekunde)

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latenz

Gemessen anhand der Zeit (in Sekunden) bis zum ersten Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Ende-zu-Ende-Antwortzeit

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Häufig gestellte Fragen

Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) ist intelligenter. Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) erzielt im Artificial Analysis Intelligence Index einen Wert von 53, verglichen mit Claude Sonnet 5 (Adaptive Reasoning, High Effort) mit 32.

Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) ist schneller. Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) erzeugt 66.7 Tokens pro Sekunde, verglichen mit Claude Sonnet 5 (Adaptive Reasoning, High Effort) mit 59.5 Tokens pro Sekunde.

Claude Sonnet 5 (Adaptive Reasoning, High Effort) ist günstiger. Claude Sonnet 5 (Adaptive Reasoning, High Effort) kostet $1.54 pro 1 Mio. Tokens, verglichen mit Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) mit $7.17 pro 1 Mio. Tokens bei einem Verhältnis von 7:2:1 für Cache-Treffer, Eingabe und Ausgabe.

Claude Sonnet 5 (Adaptive Reasoning, High Effort) hat die niedrigere Latenz. Die Zeit bis zum ersten Token beträgt bei Claude Sonnet 5 (Adaptive Reasoning, High Effort) 10.02 s, verglichen mit Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) mit 299.76 s.

Beide Modelle haben ein Kontextfenster von 1.0M Tokens.

Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) vs. Claude Sonnet 5 (Adaptive Reasoning, High Effort)

Intelligenz

Geschwindigkeit

Kosten pro Aufgabe

IntelligenzUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Benchmarks

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Vergleiche des Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Tokenverbrauch

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Kosten

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Kontextfenster

Context Window

Context Window for RAG

Context Window

Geschwindigkeit

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latenz

Latency: Time To First Answer Token

Time to First Answer Token

Ende-zu-Ende-Antwortzeit

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Häufig gestellte Fragen

Welches Modell ist intelligenter, Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) oder Claude Sonnet 5 (Adaptive Reasoning, High Effort)?

Welches Modell ist schneller, Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) oder Claude Sonnet 5 (Adaptive Reasoning, High Effort)?

Welches Modell ist günstiger, Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) oder Claude Sonnet 5 (Adaptive Reasoning, High Effort)?

Welches Modell hat die niedrigere Latenz, Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) oder Claude Sonnet 5 (Adaptive Reasoning, High Effort)?

Welches Modell hat das größere Kontextfenster, Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) oder Claude Sonnet 5 (Adaptive Reasoning, High Effort)?