This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

AI21 Labs has launched a newer model, Jamba 1.6 Large. We suggest considering it instead.

For more information, see comparison of Jamba 1.6 Large to other models and API provider benchmarks for Jamba 1.6 Large.

Jamba 1.5 Large logo

Open weights model

Released August 2024

Análisis de inteligencia, rendimiento y precio de Jamba 1.5 Large

Resumen del modelo

Inteligencia

5
Índice de Inteligencia de Artificial Analysis
1 de 4 unidades para Inteligencia.

Velocidad

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

Precio de entrada

2,00 US$
USD por 1 M de tokens
4 de 4 unidades para Precio de entrada.

Precio de salida

8,00 US$
USD por 1 M de tokens
4 de 4 unidades para Precio de salida.

Verbosidad

N/D
Tokens de salida del Índice de Inteligencia
Valor desconocido de 4 unidades para Verbosidad.

Jamba 1.5 Large está entre los modelos menos inteligentes y tiene un precio especialmente elevado en comparación con otros modelos sin razonamiento, de pesos abiertos y tamaño similar. El modelo admite entrada de texto, genera texto y tiene una ventana de contexto de 256k tokens con conocimientos hasta marzo de 2024.

Jamba 1.5 Large obtiene 5 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa en el extremo inferior entre modelos comparables (con una mediana de 17).

El precio de Jamba 1.5 Large es de $2.00 por 1 M de tokens de entrada (elevado; mediana: $0.43) y $8.00 por 1 M de tokens de salida (elevado; mediana: $1.31).

RazonamientoNo

Esta página muestra la versión sin razonamiento de este modelo.

También puede existir una variante con razonamiento.

Modalidad de entrada

Admite: texto

Modalidad de salida

Admite: texto

Fecha límite de conocimiento5 mar 2024
Ventana de contexto256k
~384 páginas A4 con fuente Arial de 12 puntos
Parámetros totales398B
Parámetros activos94B
Número de parámetros activos por token durante la inferencia
LicenciaJamba Open Model License Agreement
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

Artificial Analysis Intelligence Index · Higher is better

Velocidad

Output tokens per second · Higher is better
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
Estimate (independent evaluation forthcoming)
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
Estimate (independent evaluation forthcoming)
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).

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

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

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Scientific reasoning

Physics reasoning

Long context reasoning

Agentic knowledge work, Elo

Agentic SaaS workflows

Legal agentic work, criterion pass rate

Agentic business operations

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 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.

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.

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 Jamba 1.5 Large

Jamba 1.5 Large se lanzó el 22 de agosto de 2024.

Jamba 1.5 Large fue creado por AI21 Labs.

Jamba 1.5 Large obtiene 5 puntos (estimados) en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa en el extremo inferior entre otros modelos sin razonamiento, de pesos abiertos y tamaño similar (mediana: 17).

Jamba 1.5 Large cuesta $2.00 por 1 M de tokens de entrada (en el extremo superior; mediana: $0.60) y $8.00 por 1 M de tokens de salida (en el extremo superior; mediana: $2.50), según la mediana de los proveedores que sirven el modelo.

Jamba 1.5 Large cuesta $2.00 por 1 M de tokens de entrada y $8.00 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 $2.60 por 1 M de tokens. El precio puede variar según el proveedor. Comparar precios de proveedores

No, Jamba 1.5 Large no es un modelo de razonamiento. Responde directamente sin razonamiento extendido en cadena.

Jamba 1.5 Large admite entrada de texto.

Jamba 1.5 Large admite salida de texto.

No, Jamba 1.5 Large no admite imágenes como entrada. Solo puede procesar texto.

No, Jamba 1.5 Large no es multimodal. Solo admite entradas de texto.

Jamba 1.5 Large 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, Jamba 1.5 Large es un modelo de pesos abiertos. Los pesos del modelo están disponibles públicamente y pueden descargarse para alojarlo por cuenta propia.

Jamba 1.5 Large tiene 398 mil millones de parámetros (94 mil millones activos).

Jamba 1.5 Large es un modelo de mezcla de expertos (MoE) con 398 mil millones de parámetros totales, pero durante la inferencia solo usa 94 mil millones de parámetros activos.

Jamba 1.5 Large se publica bajo la licencia Jamba Open Model License Agreement, que permite el uso comercial. Ver licencia

Jamba 1.5 Large obtiene 5 puntos en el Índice de Inteligencia de Artificial Analysis. Este benchmark compuesto evalúa los modelos en razonamiento, conocimiento, matemáticas y programación.

La fecha límite de conocimiento de Jamba 1.5 Large es marzo de 2024. Sus datos de entrenamiento incluyen información hasta esa fecha.

Sí, Jamba 1.5 Large está disponible mediante API a través de 1 proveedor. Comparar proveedores de API

Jamba 1.5 Large está disponible a través de 1 proveedor de API. Comparar proveedores