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

DeepSeek V4.1 Flash (Reasoning, Max Effort)과 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback)의 지능, 가격, 속도, 컨텍스트 창 등을 비교합니다.

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	DeepSeek	Anthropic
지능
Intelligence Index	40	49
AA-Briefcase	1424	1531
GDPval-AA v2	1632	1579
AutomationBench-AA	69%	55%
Terminal-Bench v4.0	27%	45%
SciCode	52%	56%
Humanity's Last Exam	39%	54%
GDP.pdf	13%	27%
CritPt	14%	29%
AA-Omniscience	−5	38
AA-LCR v1.1	84%	85%
비용
토큰 100만 개당 가격	$0.1842	$7.175
입력 토큰 1M당 가격	$0.30	$10.00
출력 토큰 1M당 가격	$1.20	$50.00
캐시 히트 토큰 1M당 가격	$0.006	$0.25
작업당 비용	$0.27	$2.98
Intelligence Index 실행 비용	US$477	US$3,983
토큰 사용량
작업당 출력 토큰	89k	28k
작업당 추론 토큰	63k	12k
Intelligence Index 실행 출력 토큰	253M	44M
성능
출력 속도	190토큰/초	47토큰/초
첫 토큰까지 걸린 시간	1.00초	12.73초
첫 답변 토큰까지의 시간	11.52초	12.73초
종단 간 응답 시간	14.15초	23.37초
작업당 시간	415.38초	351.29초
기술 사양
컨텍스트 창	1000k토큰Arial 12포인트 기준 A4 약 1,500페이지	1000k토큰Arial 12포인트 기준 A4 약 1,500페이지
출시일	2026년 9월	2026년 9월
총 파라미터 수	552B
활성 파라미터 수	16B
추론	예	예
입력 모달리티	지원: 텍스트 및 이미지	지원: 텍스트 및 이미지
출력 모달리티	지원: 텍스트	지원: 텍스트
오픈 웨이트	예	아니요
라이선스	Mit
라이선스가 제한 없는 상업적 이용 허용	예

주요 내용

Updated

지능

Artificial Analysis Intelligence Index · Higher is better

속도

Output tokens per second · Higher is better

작업당 비용

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

지능Updated

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.

벤치마크

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.

Openness Index

Artificial Analysis Openness Index: Score

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

Intelligence Index 비교

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.

토큰 사용량

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).

비용

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.

컨텍스트 창

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).

속도

출력 속도(초당 토큰 수)로 측정

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.

지연 시간

첫 토큰까지 걸린 시간(초)으로 측정

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.

종단 간 응답 시간

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).

모델 크기(오픈 웨이트 모델만 해당)

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.

자주 묻는 질문

지능이 더 높은 모델은 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback)입니다. Artificial Analysis Intelligence Index에서 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 모델은 49점, DeepSeek V4.1 Flash (Reasoning, Max Effort) 모델은 40점을 받았습니다.

더 빠른 모델은 DeepSeek V4.1 Flash (Reasoning, Max Effort)입니다. DeepSeek V4.1 Flash (Reasoning, Max Effort) 모델은 초당 190.1토큰, Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 모델은 초당 47.0토큰을 생성합니다.

더 저렴한 모델은 DeepSeek V4.1 Flash (Reasoning, Max Effort)입니다. 토큰 100만 개당 DeepSeek V4.1 Flash (Reasoning, Max Effort) 모델은 $0.18, Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 모델은 $7.17입니다(캐시 적중/입력/출력 비율 7:2:1).

지연 시간이 더 짧은 모델은 DeepSeek V4.1 Flash (Reasoning, Max Effort)입니다. 첫 토큰까지 DeepSeek V4.1 Flash (Reasoning, Max Effort) 모델은 1.00초, Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 모델은 12.73초가 걸립니다.

두 모델 모두 컨텍스트 창이 1.0M토큰입니다.

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

지능

속도

작업당 비용

지능Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

벤치마크

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

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index 비교

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

토큰 사용량

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

비용

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

컨텍스트 창

Context Window

Context Window for RAG

Context Window

속도

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

지연 시간

Latency: Time To First Answer Token

Time to First Answer Token

종단 간 응답 시간

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

모델 크기(오픈 웨이트 모델만 해당)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

자주 묻는 질문

DeepSeek V4.1 Flash (Reasoning, Max Effort)과 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 중 어느 모델의 지능이 더 높은가요?

DeepSeek V4.1 Flash (Reasoning, Max Effort)과 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 중 어느 모델이 더 빠른가요?

DeepSeek V4.1 Flash (Reasoning, Max Effort)과 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 중 어느 모델이 더 저렴한가요?

DeepSeek V4.1 Flash (Reasoning, Max Effort)과 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 중 어느 모델의 지연 시간이 더 짧은가요?

DeepSeek V4.1 Flash (Reasoning, Max Effort)과 Claude Fable 5.1 (Adaptive Reasoning, Medium Effort, Default Fallback) 중 어느 모델의 컨텍스트 창이 더 큰가요?