독립적인 AI 분석

AI 환경을 이해하고 사용 사례에 가장 적합한 모델과 제공업체를 선택하세요.

주요 내용

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

맞춤형 모델 추천

지능, 속도, 비용 우선순위에 따른 맞춤형 추천을 받아보세요.

일반 업무, 코딩, 고객 지원 등의 에이전트 살펴보기

역량, 가격, 플랫폼 지원을 기준으로 AI 에이전트를 비교하세요.

프리미엄 요금제 살펴보기

확장된 벤치마크 데이터, 맞춤형 시각화, 산업 보고서 등을 이용하세요.

변경 기록
새 언어 모델 평가 · 8월 3일
G9v3-39A5BG9v3-39A5B
새 아티클 게시 · 7월 31일
DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, 10 points above previous DeepSeek V4 Flash
새 언어 모델 평가 · 7월 31일
Celeris-1Celeris-1
새 언어 모델 평가 · 7월 31일
DeepSeek V4 Flash 0731 (Reasoning, Max Effort)DeepSeek V4 Flash 0731 (Reasoning, Max Effort)
새 아티클 게시 · 7월 30일
Inkling Small lands within a point of Inkling on the Artificial Analysis Intelligence Index with less than a third of the parameters
방법론 업데이트 · 7월 30일
Artificial AnalysisWe have updated our Cost per Task methodology, resulting in slight absolute increases in cost estimates but with minimal impact on relative positioning.
새 언어 모델 평가 · 7월 30일
Kimi K3 (low)Kimi K3 (low)
새 언어 모델 평가 · 7월 30일
Inkling SmallInkling Small
새 아티클 게시 · 7월 29일
Agnes AI releases Agnes 2.5 Pro Alpha
새 아티클 게시 · 7월 24일
Claude Opus 5: the new leader in agentic knowledge work
새 아티클 게시 · 7월 24일
Opus 5: Fable 5 level intelligence at a lower cost per task
새 언어 모델 평가 · 7월 24일
Claude Opus 5 (Adaptive Reasoning, Low Effort)Claude Opus 5 (Adaptive Reasoning, Low Effort)
새 언어 모델 평가 · 7월 24일
Claude Opus 5 (Adaptive Reasoning, Medium Effort)Claude Opus 5 (Adaptive Reasoning, Medium Effort)
새 언어 모델 평가 · 7월 24일
Claude Opus 5 (Adaptive Reasoning, High Effort)Claude Opus 5 (Adaptive Reasoning, High Effort)
새 언어 모델 평가 · 7월 24일
Claude Opus 5 (Adaptive Reasoning, Xhigh Effort)Claude Opus 5 (Adaptive Reasoning, Xhigh Effort)
새 언어 모델 평가 · 7월 24일
Claude Opus 5 (Adaptive Reasoning, Max Effort)Claude Opus 5 (Adaptive Reasoning, Max Effort)
새 언어 모델 평가 · 7월 23일
Agnes 2.5 Pro AlphaAgnes 2.5 Pro Alpha
새 아티클 게시 · 7월 22일
How Thinking Machines Lab’s Inkling performs on agentic knowledge work
새 언어 모델 평가 · 7월 22일
G9v3-3BG9v3-3B
새 아티클 게시 · 7월 21일
Kimi K3: second only to Fable 5 on AA-Briefcase더 보기

지능

독립적인 평가를 바탕으로 한 주요 AI 모델의 지능

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. 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

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

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.

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
Reasoning models are indicated by a lightbulb icon

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.

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

시간에 따른 최첨단 언어 모델 지능

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Coding Agent Index

종단 간 소프트웨어 엔지니어링 작업에서 주요 코딩 에이전트의 성능, 비용, 실행 시간

Artificial Analysis Coding Agent Index

Composite average pass@1 across DeepSWE, Terminal-Bench v2, and SWE-Atlas-QnA · Higher is better

이미지 및 동영상

95% 신뢰 구간과 함께 제공되는 Image Arena 및 Video Arena 리더보드의 상위 모델

텍스트-이미지 생성 리더보드

Image Arena의 블라인드 선호도 투표에서 산출한 Elo 점수입니다. 전체 리더보드를 확인하세요.

음성

Text to Speech Arena, 음성 텍스트 변환, 음성 대 음성 평가의 상위 모델

Text to Speech Arena Leaderboard

Elo scores from blind preference votes in our Text to Speech Arena · See the full leaderboard here.

Relative Elo score of the models as determined by responses from users in Artificial Analysis' Speech Arena. Some models may not be shown due to not yet having enough votes.

역량 지수Updated

더 보기

특정 역량 및 산업에서 모델의 성능을 측정합니다.

Artificial Analysis Agentic Index

Measures performance in agentic workflows, focusing on behaviors like tool use, planning, autonomy, and complex problem solving.
Reasoning models are indicated by a lightbulb icon

벤치마크

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

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

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

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

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

AA-Briefcase

AA-Briefcase는 스프레드시트, 프레젠테이션, 메모 같은 결과물을 요구하는 실제 비즈니스 워크플로에서 에이전트를 테스트하는 장기 지식 업무용 최첨단 에이전트 평가입니다.

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
Reasoning models are indicated by a lightbulb icon

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-Omniscience는 정확한 답변에 보상하고 잘못된 추측에는 감점을 부여하여 여러 분야에서 사실에 근거한 신뢰할 수 있는 출력을 생성하는 모델을 종합적으로 보여 주는 지식 및 환각 벤치마크입니다.

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.
Reasoning models are indicated by a lightbulb icon

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.

GDPval-AA v2

GDPval-AA v2은 다양한 직종의 실제 경제적 가치가 있는 작업에서 AI 모델을 평가합니다.

GDPval-AA v2 Leaderboard

Elo rating for performance on real-world work tasks · Anchored to a human baseline of 1,000 · Higher is better
Human Baseline (1,000)
Reasoning models are indicated by a lightbulb icon

Openness Index

Artificial Analysis Openness Index는 여러 구성 요소의 가용성과 투명성을 바탕으로 모델이 얼마나 '개방적'인지 평가합니다.

Artificial Analysis Openness Index: Components

Openness Index underlying score contribution by components, up to a maximum of 18 (higher is more open)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Openness Index vs. Artificial Analysis Intelligence Index

Most attractive quadrant
Pareto line

출력 토큰

독립적인 평가를 바탕으로 한 주요 AI 모델의 출력 토큰 수

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

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

독립적인 평가를 바탕으로 한 주요 AI 모델의 가격 및 실제 비용

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

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.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

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)
Reasoning models are indicated by a lightbulb icon

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.

속도 및 지연 시간

자체 API 성능 비교

Output Speed

Output tokens per second · Higher is better
Reasoning models are indicated by a lightbulb icon

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
Reasoning models are indicated by a lightbulb icon

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.

제공업체

Endpoint Accuracy Index: gpt-oss-120b (high)

v1.0 · Composite of BFCL v4-500, HLE-250 and AA-LCR-25 run against each provider endpoint · Percentage of the reference endpoint, with 95% confidence interval · Higher is better

Composite measure of how much of a model's accuracy a given provider endpoint preserves, from re-running BFCL v4-500, HLE-250 and AA-LCR-25 against that endpoint. Where a self-hosted reference endpoint exists, scores are expressed as a percentage of that reference (100 = matches reference); lower scores indicate accuracy lost to quantisation, sampling defaults, or other endpoint-side configuration. Scores are point-in-time snapshots. Methodology.

Output Speed vs. Price: gpt-oss-120b (high)

Output tokens per second · USD per 1M tokens (blended) · 10,000 input tokens
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Smaller, emerging providers are offering high output speed and at competitive prices.

가격(캐시 적중, 입력, 출력): gpt-oss-120b (high)

Price (USD per M Tokens) · Lower is better · 10,000 input 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.

Output Speed: gpt-oss-120b (high)

Output speed: output tokens per second · 10,000 input tokens

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).