DeepSeek V4.1 Flash (Reasoning, Max Effort) vs. Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)

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

Comparación entre DeepSeek V4.1 Flash (Reasoning, Max Effort) y Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback) según inteligencia, precio, velocidad, ventana de contexto y más.

Para más detalles sobre nuestra metodología, consulta la página de metodología.

	DeepSeek	Anthropic
Inteligencia
Índice de Inteligencia	40	51
AA-Briefcase	1424	1578
GDPval-AA v2	1632	1650
AutomationBench-AA	69%	55%
Terminal-Bench v4.0	27%	52%
SciCode	52%	59%
Humanity's Last Exam	39%	56%
GDP.pdf	13%	27%
CritPt	14%	30%
AA-Omniscience	−5	41
AA-LCR v1.1	84%	84%
Costo
Precio por millón de tokens	$0.1842	$7.175
Precio de entrada por 1M de tokens	$0.30	$10.00
Precio de salida por 1M de tokens	$1.20	$50.00
Precio de acierto de caché por 1M de tokens	$0.006	$0.25
Costo por tarea	$0.27	$3.91
Costo de ejecutar el Intelligence Index	477 US$	5242 US$
Uso de tokens
Tokens de salida por tarea	89k	38k
Tokens de razonamiento por tarea	63k	19k
Tokens de salida para ejecutar el Intelligence Index	253M	62M
Rendimiento
Velocidad de salida	190 tokens/s	49 tokens/s
Tiempo hasta el primer token	1.00s	22.79s
Tiempo hasta el primer token de respuesta	11.52s	22.79s
Tiempo de respuesta de extremo a extremo	14.15s	33.06s
Tiempo por tarea	415.38s	469.39s
Especificaciones técnicas
Ventana de contexto	1000k tokens~1500 páginas A4 con fuente Arial de tamaño 12	1000k tokens~1500 páginas A4 con fuente Arial de tamaño 12
Fecha de lanzamiento	septiembre de 2026	septiembre de 2026
Parámetros totales	552B
Parámetros activos	16B
Razonamiento	Sí	Sí
Modalidad de entrada	Admite: texto e imágenes	Admite: texto e imágenes
Modalidad de salida	Admite: texto	Admite: texto
Pesos abiertos	Sí	No
Licencia	Mit
La licencia permite el uso comercial sin restricciones	Sí

Aspectos destacados

Updated

Inteligencia

Artificial Analysis Intelligence Index · Higher is better

Velocidad

Output tokens per second · Higher is better

Costo por tarea

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

InteligenciaUpdated

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.

Índice de apertura

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Comparaciones del Índice de Inteligencia

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.

Uso de tokens

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).

Costo

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.

Ventana de contexto

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).

Velocidad

Medida por la velocidad de salida (tokens por segundo)

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.

Latencia

Medida por el tiempo (segundos) hasta el primer 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.

Tiempo de respuesta de extremo a extremo

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).

Tamaño del modelo (solo modelos de pesos abiertos)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Preguntas frecuentes

Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback) es más inteligente. Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback) obtiene 51, frente a los 40 de DeepSeek V4.1 Flash (Reasoning, Max Effort) en el Índice de Inteligencia de Artificial Analysis.

DeepSeek V4.1 Flash (Reasoning, Max Effort) es más rápido. DeepSeek V4.1 Flash (Reasoning, Max Effort) genera 190.1 tokens por segundo, frente a los 48.7 tokens por segundo de Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback).

DeepSeek V4.1 Flash (Reasoning, Max Effort) es más económico. DeepSeek V4.1 Flash (Reasoning, Max Effort) cuesta $0.18 por millón de tokens, frente a Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback), que cuesta $7.17 por millón de tokens (proporción 7:2:1 entre aciertos de caché, entrada y salida).

DeepSeek V4.1 Flash (Reasoning, Max Effort) tiene menor latencia. El tiempo hasta el primer token de DeepSeek V4.1 Flash (Reasoning, Max Effort) es de 1.00s, frente a los 22.79s de Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback).

Ambos modelos tienen una ventana de contexto de 1.0M tokens.

DeepSeek V4.1 Flash (Reasoning, Max Effort) vs. Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)

Inteligencia

Velocidad

Costo por tarea

InteligenciaUpdated

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

Índice de apertura

Artificial Analysis Openness Index: Score

Comparaciones del Índice de Inteligencia

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Uso de tokens

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Costo

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

Ventana de contexto

Context Window

Context Window for RAG

Context Window

Velocidad

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latencia

Latency: Time To First Answer Token

Time to First Answer Token

Tiempo de respuesta de extremo a extremo

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Tamaño del modelo (solo modelos de pesos abiertos)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Preguntas frecuentes

¿Cuál es más inteligente, DeepSeek V4.1 Flash (Reasoning, Max Effort) o Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)?

¿Cuál es más rápido, DeepSeek V4.1 Flash (Reasoning, Max Effort) o Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)?

¿Cuál es más económico, DeepSeek V4.1 Flash (Reasoning, Max Effort) o Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)?

¿Cuál tiene menor latencia, DeepSeek V4.1 Flash (Reasoning, Max Effort) o Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)?

¿Cuál tiene una ventana de contexto más grande, DeepSeek V4.1 Flash (Reasoning, Max Effort) o Claude Fable 5.1 (Adaptive Reasoning, High Effort, Default Fallback)?