Destacados

Artificial Analysis Intelligence Index · Higher is better
Output tokens per second · Higher is better
Weighted average cost (USD) per Intelligence Index task · Lower is better
Nuevo artículo publicado · 13 ago
Gemini 3.7 Flash: On the Intelligence vs. Time per Task Pareto frontier
Nuevo artículo publicado · 13 ago
Announcing Optima: create a custom benchmark for your use case
Nueva evaluación de modelo de lenguaje · 13 ago
Qwen3.8 2.4T A95BQwen3.8 2.4T A95B
Nueva evaluación de modelo de lenguaje · 13 ago
Gemini 3.7 Flash (medium)Gemini 3.7 Flash (medium)
Nueva evaluación de modelo de lenguaje · 13 ago
Gemini 3.7 Flash (low)Gemini 3.7 Flash (low)
Nueva evaluación de modelo de lenguaje · 13 ago
Gemini 3.7 Flash (high)Gemini 3.7 Flash (high)
Nuevo artículo publicado · 12 ago
Upstage Solar Pro 4: Benchmarks and analysis
Nuevo artículo publicado · 12 ago
Grok 4.6 returns SpaceXAI to the intelligence frontier and leads on cost efficiency
Nueva evaluación de modelo de lenguaje · 12 ago
DeepSeek V4 Pro 0813 (Reasoning, Max Effort)DeepSeek V4 Pro 0813 (Reasoning, Max Effort)
Nueva evaluación de modelo de lenguaje · 12 ago
Solar Open2 250BSolar Open2 250B
Nueva evaluación de modelo de lenguaje · 12 ago
A.X-K2A.X-K2
Nueva evaluación de modelo de lenguaje · 12 ago
Motif 3Motif 3
Nueva evaluación de modelo de lenguaje · 12 ago
K-EXAONE 2.0 0803K-EXAONE 2.0 0803
Nueva evaluación de modelo de lenguaje · 12 ago
Solar Pro 4Solar Pro 4
Nueva evaluación de modelo de lenguaje · 12 ago
Grok 4.6 (high)Grok 4.6 (high)
Nuevo artículo publicado · 11 ago
NVIDIA launches Nemotron 3.5 Lightning
Nueva evaluación de modelo de lenguaje · 11 ago
Nemotron 3.5 LightningNemotron 3.5 Lightning
Nuevo artículo publicado · 10 ago
Muse Glimmer: Benchmarks and analysis
Nueva evaluación de modelo de lenguaje · 10 ago
Muse Glimmer (high)Muse Glimmer (high)
Nuevo artículo publicado · 6 ago
Launching v4.1.1 of the Artificial Analysis Intelligence IndexVer más

Inteligencia

Inteligencia de los principales modelos de IA según nuestras evaluaciones independientes

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1.1 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 v4.1.1 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 v4.1.1 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 v4.1.1 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 v4.1.1 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.

Inteligencia de modelos de lenguaje de frontera a lo largo del tiempo

Artificial Analysis Intelligence Index v4.1.1 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 v4.1.1 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.

Rendimiento, costo y tiempo de ejecución de los principales agentes de programación en tareas integrales de ingeniería de software

Índice de agentes de programación de Artificial Analysis

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

Imagen y video

Mejores modelos de nuestros rankings Image Arena y Video Arena, con intervalos de confianza del 95%

Ranking de texto a imagen

Puntuaciones Elo a partir de votos ciegos de preferencia en nuestro Image Arena. Ver el ranking completo aquí.

Voz

Mejores modelos de nuestras evaluaciones Text to Speech Arena, Speech to Text y Speech to Speech

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.

Miden el rendimiento de modelos en capacidades e industrias específicas

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

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
See more

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

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Scientific reasoning

Physics reasoning

Knowledge

1 - hallucination rate

AA-LCRUpdated

Long context reasoning

Agentic knowledge work, Elo

Agentic SaaS workflows

Legal agentic work, criterion pass rate

Agentic business operations

Quantitative analysis on spreadsheets & documents

Instruction following

Long-horizon agentic tasks

Kubernetes incident root-cause analysis

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 v4.1.1 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-Briefcase es una evaluación agéntica de vanguardia para trabajo cognitivo de largo plazo, que prueba agentes en flujos de trabajo empresariales realistas que requieren entregables como hojas de cálculo, presentaciones y memorandos

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-AnalystAgent

AA-AnalystAgent es un benchmark de análisis cuantitativo de extremo a extremo sobre hojas de cálculo y documentos reales: el tipo de trabajo que los analistas de negocio y de datos realizan a diario

AA-AnalystAgent pass^5

Share of end-to-end quantitative analysis tasks solved on all five attempts · Higher is better
Reasoning models are indicated by a lightbulb icon

Share of AA-AnalystAgent questions answered correctly on all five attempts. AA-AnalystAgent is Artificial Analysis' benchmark for end-to-end quantitative analysis on real-world spreadsheets and documents; every question is run five independent times, so pass^5 measures how reliably a model reproduces a correct answer rather than how often it reaches one.

AA-Omniscience

AA-Omniscience es un benchmark de conocimiento y alucinaciones que premia la precisión, penaliza las respuestas incorrectas y ofrece una vista completa de qué modelos producen resultados fiables en distintos dominios

Índice AA-Omniscience

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 v2 evalúa modelos de IA en tareas reales de valor económico en una amplia gama de ocupaciones

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

El índice de apertura de Artificial Analysis evalúa qué tan 'abiertos' son los modelos según su disponibilidad y transparencia en distintos componentes.

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

Tokens de salida

Tokens de salida de los principales modelos de IA según nuestras evaluaciones independientes

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).

Costo

Precios y costos reales de los principales modelos de IA según nuestras evaluaciones independientes

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.

Velocidad y latencia

Comparación del rendimiento de API de primera parte

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.

Proveedores

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.

Precios (acierto de caché, entrada y salida): 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).