Agnes 2.5 Pro Alpha vs. GLM-5.2 (max)

Comparación entre Agnes 2.5 Pro Alpha y GLM-5.2 (max) según inteligencia, precio, velocidad, ventana de contexto y más.

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

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

Comparación de modelos

Logo de Sapiens AISapiens AI
Agnes 2.5 Pro Alpha
Logo de Z AIZ AI
GLM-5.2 (max)
Índice de Inteligencia
31*
43*
GLM-5.2 (max) es más inteligente que Agnes 2.5 Pro Alpha
Precio por millón de tokens
$0.19
$0.90
Agnes 2.5 Pro Alpha es más económico que GLM-5.2 (max)
Velocidad de salida
195 tokens/s
73 tokens/s
Agnes 2.5 Pro Alpha es más rápido que GLM-5.2 (max)
Tiempo hasta el primer token
2.73s
1.67s
GLM-5.2 (max) responde más rápido que Agnes 2.5 Pro Alpha
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
Tanto Agnes 2.5 Pro Alpha como GLM-5.2 (max) tienen una ventana de contexto del mismo tamaño
Fecha de lanzamiento
julio de 2026
junio de 2026
Agnes 2.5 Pro Alpha tiene una fecha de lanzamiento más reciente que la de GLM-5.2 (max)
Parámetros
397B, 17B activos durante la inferencia
753B, 40B activos durante la inferencia
GLM-5.2 (max) tiene más parámetros que Agnes 2.5 Pro Alpha
Razonamiento
Sí
Sí
Tanto Agnes 2.5 Pro Alpha como GLM-5.2 (max) son modelos de razonamiento
Entrada de imágenes
Sí
No
Agnes 2.5 Pro Alpha admite imágenes como entrada, mientras que GLM-5.2 (max) no
Pesos abiertos
Sí
Sí
Tanto Agnes 2.5 Pro Alpha como GLM-5.2 (max) tienen pesos abiertos
Licencia
Apache 2.0
Mit
La licencia permite el uso comercial sin restricciones
Sí
Sí
Tanto la licencia de Agnes 2.5 Pro Alpha como la de GLM-5.2 (max) permiten el uso comercial sin restricciones

InteligenciaUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.2 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, 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

Artificial Analysis Intelligence Index v4.2 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, 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.

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
See more
AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

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

GDP.pdfNew

Professional document reasoning, All-pass

No data available
CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

GPQA Diamond

Scientific reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

No data available
IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

No data available

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

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, 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.

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.

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
No data available

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 v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, 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.

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
No data available

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
No data available

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
No data available

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
No data available

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

GLM-5.2 (max) es más inteligente. GLM-5.2 (max) obtiene 43 (estimado), frente a los 31 (estimados) de Agnes 2.5 Pro Alpha en el Índice de Inteligencia de Artificial Analysis.

Agnes 2.5 Pro Alpha es más rápido. Agnes 2.5 Pro Alpha genera 195.5 tokens por segundo, frente a los 73.1 tokens por segundo de GLM-5.2 (max).

Agnes 2.5 Pro Alpha es más económico. Agnes 2.5 Pro Alpha cuesta $0.19 por millón de tokens, frente a GLM-5.2 (max), que cuesta $0.90 por millón de tokens (proporción 7:2:1 entre aciertos de caché, entrada y salida).

GLM-5.2 (max) tiene menor latencia. El tiempo hasta el primer token de GLM-5.2 (max) es de 1.67s, frente a los 2.73s de Agnes 2.5 Pro Alpha.

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