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 Results for other workloads are historical and no longer updated.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 release, Jamba 1.7 Large. We suggest considering it instead.

AI21 Labs

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Open weights model

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Released March 2025

Jamba 1.6 Large Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out API Provider Benchmarks

Intelligence

Artificial Analysis Intelligence Index

* Estimated

Speed

N/A

Output tokens per second

Cost

In -Out -

N/A

Cost per Intelligence Index task

Verbosity

N/A

Output tokens from Intelligence Index

Jamba 1.6 Large is among the least intelligent models when comparing to other open weight non-reasoning models of similar size. The model supports text input, outputs text, and has a 256k tokens context window.

Jamba 1.6 Large scores 6 on the Artificial Analysis Intelligence Index, placing it at the lower end among comparable models (median: 12).

Reasoning	No This page shows the non-reasoning version of this model. A reasoning variant may also exist.
Input modality	Supports: text
Output modality	Supports: text
Context window	256k ~384 A4 pages of size 12 Arial font
Total parameters	398B
Active parameters	94B Number of parameters active per token during inference
License	Jamba Open Model License Agreement
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

No publicly available API

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, 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

No publicly available API

Estimate (independent evaluation forthcoming)

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.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

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.

Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

No publicly available API

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

GDPval-AA v2.1Updated

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

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

AA-LCR v1.1

Long context reasoning

SciCodeUnder review

Coding

CritPtUnder review

Physics reasoning

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 1.1New

Agentic software engineering in open-source repositories

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AA

Medical long context reasoning

MMMU-Pro

Visual reasoning

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

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence Index Comparisons

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

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.

Context Window

Context window: tokens limit · Higher is better

No publicly available API

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).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

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.

Frequently Asked Questions

Common questions about Jamba 1.6 Large

Jamba 1.6 Large was released on March 6, 2025.

Jamba 1.6 Large was created by AI21 Labs.

Jamba 1.6 Large scores 6 (estimated) on the Artificial Analysis Intelligence Index, placing it at the lower end among other open weight non-reasoning models of similar size (median: 12).

No, Jamba 1.6 Large is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.

Jamba 1.6 Large supports text input.

Jamba 1.6 Large supports text output.

No, Jamba 1.6 Large does not support image input. It can only process text.

No, Jamba 1.6 Large is not multimodal. It only supports text input.

Jamba 1.6 Large has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Jamba 1.6 Large is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Jamba 1.6 Large has 398 billion parameters (94 billion active).

Jamba 1.6 Large is a Mixture of Experts (MoE) model with 398 billion total parameters, but only 94 billion active parameters are used during inference.

Jamba 1.6 Large is released under the Jamba Open Model License Agreement license. Commercial use is allowed with restrictions. View license

Jamba 1.6 Large achieves a score of 6 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Jamba 1.6 Large is an open weights model that can be self-hosted. View providers

Jamba 1.6 Large is an open weights model that can be downloaded and self-hosted. Compare providers

Jamba 1.6 Large Intelligence, Performance & Price Analysis

Model summary

Intelligence

Speed

Cost

Verbosity

Comparison Summary

Technical specifications

46 models in this class

Intelligence

Speed

Cost per Task

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Benchmarks

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index Comparisons

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

When was Jamba 1.6 Large released?

Who created Jamba 1.6 Large?

How intelligent is Jamba 1.6 Large?

Is Jamba 1.6 Large a reasoning model?

What input modalities does Jamba 1.6 Large support?

What output modalities does Jamba 1.6 Large support?

Can Jamba 1.6 Large process images?

Is Jamba 1.6 Large multimodal?

What is the context window of Jamba 1.6 Large?

Is Jamba 1.6 Large open source?

How many parameters does Jamba 1.6 Large have?

What are the active parameters of Jamba 1.6 Large?

What is the license for Jamba 1.6 Large?

How does Jamba 1.6 Large perform on benchmarks?

Is Jamba 1.6 Large available via API?

Where can I use Jamba 1.6 Large?