Gemma 4 26B A4B (Reasoning) logo
Google
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Open weights model

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Released April 2026

Análisis de inteligencia, rendimiento y precio de Gemma 4 26B A4B (Reasoning)

Probar Benchmarks de proveedores de API

Resumen del modelo

Inteligencia

26
Índice de Inteligencia de Artificial Analysis
4 de 4 unidades para Inteligencia.

Velocidad

N/D
Tokens de salida por segundo
Valor desconocido de 4 unidades para Velocidad.

Precio

Entrada
0,13 US$
por 1 M de tokens
Salida
0,40 US$
por 1 M de tokens
3 de 4 unidades para Precio.

Precio de acierto de caché

0,085 US$
USD por 1 M de tokens
2 de 4 unidades para Precio de acierto de caché.

Verbosidad

74M
Tokens de salida del Índice de Inteligencia
4 de 4 unidades para Verbosidad.

Gemma 4 26B A4B (Reasoning) está entre los modelos líderes en inteligencia, pero tiene un precio algo elevado en comparación con otros modelos de pesos abiertos y tamaño similar. El modelo admite entrada de texto, imágenes y video, genera texto y tiene una ventana de contexto de 256k tokens.

Gemma 4 26B A4B (Reasoning) obtiene 26 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa muy por encima del promedio entre modelos comparables (con una mediana de 9). Al evaluar el Índice de Inteligencia, generó 74M tokens, lo que es muy verboso frente a la mediana de 37M.

El precio de Gemma 4 26B A4B (Reasoning) es de $0.13 por 1 M de tokens de entrada (algo elevado; mediana: $0.05) y $0.40 por 1 M de tokens de salida (algo elevado; mediana: $0.15). En total, evaluar Gemma 4 26B A4B (Reasoning) en el Índice de Inteligencia costó $59.57.

RazonamientoSí

Esta página muestra la versión con razonamiento de este modelo.

También puede existir una variante sin razonamiento.

Modalidad de entrada

Admite: texto, imágenes y video

Modalidad de salida

Admite: texto

Ventana de contexto256k
~384 páginas A4 con fuente Arial de 12 puntos
Parámetros totales25.2B
Parámetros activos3.8B
Número de parámetros activos por token durante la inferencia
LicenciaApache 2.0
Pesos del modeloHugging Face

Las métricas se comparan con modelos de la misma clase:

  • Modelos sin razonamiento → se comparan solo con otros modelos sin razonamiento
  • Modelos de razonamiento → se comparan tanto con modelos de razonamiento como sin razonamiento
  • Modelos de pesos abiertos → se comparan solo con otros modelos de pesos abiertos de la misma categoría de tamaño:
    • Muy pequeño: ≤4B parámetros
    • Pequeño: 4B–40B parámetros
    • Mediano: 40B–150B parámetros
    • Grande: >150B parámetros
  • Modelos propietarios → se comparan con modelos propietarios y de pesos abiertos del mismo rango de precio mediante una proporción combinada de precios de entrada/salida de 3:1:
    • <$0.15 por 1 M de tokens
    • $0.15–$1 por 1 M de tokens
    • >$1 por 1 M de tokens

Aspectos destacados

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

Inteligencia

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).

Benchmarks

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-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.
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.

Índice de apertura

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
Reasoning models are indicated by a lightbulb icon

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
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.

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
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

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.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive).
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Price per token generated by the model (received from the API), represented as USD per million Tokens.

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).

Ventana de contexto

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

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).

Tamaño del modelo (solo modelos de pesos abiertos)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
Reasoning models are indicated by a lightbulb icon

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

Preguntas comunes sobre Gemma 4 26B A4B (Reasoning)

Gemma 4 26B A4B (Reasoning) se lanzó el 2 de abril de 2026.

Gemma 4 26B A4B (Reasoning) fue creado por Google.

Gemma 4 26B A4B (Reasoning) obtiene 26 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa muy por encima del promedio entre otros modelos de pesos abiertos y tamaño similar (mediana: 9).

Gemma 4 26B A4B (Reasoning) cuesta $0.13 por 1 M de tokens de entrada (mejor que el promedio; mediana: $0.18) y $0.40 por 1 M de tokens de salida (mejor que el promedio; mediana: $0.40), según la mediana de los proveedores que sirven el modelo.

Gemma 4 26B A4B (Reasoning) cuesta $0.13 por 1 M de tokens de entrada y $0.40 por 1 M de tokens de salida (según la mediana de los proveedores que sirven el modelo). Con una tarifa combinada (proporción 7:2:1 entre aciertos de caché, entrada y salida), equivale a $0.13 por 1 M de tokens. El precio puede variar según el proveedor. Comparar precios de proveedores

Al evaluarlo en el Índice de Inteligencia, Gemma 4 26B A4B (Reasoning) generó 74M tokens de salida, un valor algo mayor que el promedio frente a otros modelos de pesos abiertos y tamaño similar (mediana: 37M).

Sí, Gemma 4 26B A4B (Reasoning) es un modelo de razonamiento. Usa pensamiento extendido o razonamiento en cadena para resolver problemas complejos antes de dar una respuesta.

Gemma 4 26B A4B (Reasoning) admite entrada de texto, imágenes y video.

Gemma 4 26B A4B (Reasoning) admite salida de texto.

Sí, Gemma 4 26B A4B (Reasoning) admite imágenes como entrada y puede analizarlas, describirlas y responder preguntas sobre ellas.

Sí, Gemma 4 26B A4B (Reasoning) es multimodal. Puede procesar entradas de texto, imágenes y video y generar salidas de texto.

Gemma 4 26B A4B (Reasoning) tiene una ventana de contexto de 260k tokens. Esto determina cuánto texto e historial de conversación puede procesar el modelo en una sola solicitud.

Sí, Gemma 4 26B A4B (Reasoning) es un modelo de pesos abiertos. Los pesos del modelo están disponibles públicamente y pueden descargarse para alojarlo por cuenta propia.

Gemma 4 26B A4B (Reasoning) tiene 25,2 mil millones de parámetros (3,8 mil millones activos).

Gemma 4 26B A4B (Reasoning) es un modelo de mezcla de expertos (MoE) con 25,2 mil millones de parámetros totales, pero durante la inferencia solo usa 3,8 mil millones de parámetros activos.

Gemma 4 26B A4B (Reasoning) se publica bajo la licencia Apache 2.0, que permite el uso comercial. Ver licencia

Gemma 4 26B A4B (Reasoning) obtiene 26 puntos en el Índice de Inteligencia de Artificial Analysis. Este benchmark compuesto evalúa los modelos en razonamiento, conocimiento, matemáticas y programación.

Sí, Gemma 4 26B A4B (Reasoning) está disponible mediante API a través de 8 proveedores. Comparar proveedores de API

Gemma 4 26B A4B (Reasoning) está disponible a través de 8 proveedores de API. Comparar proveedores