Jamba 1.6 Large (non-reasoning)

AI21 Labs' Open-Weight Contender

Jamba 1.6 Large (non-reasoning)

An open-weight, non-reasoning model from AI21 Labs, Jamba 1.6 Large offers a massive context window but struggles with intelligence and cost efficiency.

Open-WeightText-to-Text256k ContextHigh LatencyExpensive OutputFast Output

Jamba 1.6 Large, developed by AI21 Labs, positions itself as an open-weight model with a remarkably expansive 256k token context window. This model is designed for text-to-text generation tasks, offering a compelling option for applications requiring the processing of extensive documents or maintaining long conversational histories. Its open-weight nature provides developers with flexibility for fine-tuning and deployment in diverse environments, a significant advantage for specialized use cases.

However, an in-depth analysis reveals a nuanced performance profile. While Jamba 1.6 Large boasts an above-average output speed of 51 tokens per second, making it efficient for high-throughput generation, its intelligence metrics place it at the lower end of the spectrum. Scoring 14 on the Artificial Analysis Intelligence Index, it ranks 28th out of 30 models benchmarked, suggesting limitations in complex reasoning or nuanced understanding compared to its peers.

The cost structure for Jamba 1.6 Large presents a notable challenge. With an input token price of $2.00 per 1M tokens and an output token price of $8.00 per 1M tokens, it is considerably more expensive than the average for both input and output. This pricing model, particularly the high cost for output tokens, can lead to rapidly escalating expenses for applications that generate verbose responses or require extensive text generation. The blended price of $3.50 per 1M tokens (based on a 3:1 input-to-output ratio) further underscores its position as a premium-priced offering in the market.

Despite its cost and lower intelligence score, Jamba 1.6 Large's massive context window and respectable output speed make it a potential candidate for specific applications where the ability to handle vast amounts of information is paramount, and the complexity of reasoning required is moderate. Its suitability hinges on a careful evaluation of task requirements against its performance and pricing characteristics.

Scoreboard

Intelligence

14 (28 / 30 / Among 30 models)

Scores low on the Artificial Analysis Intelligence Index, indicating limited reasoning capabilities and suitability for complex tasks.

Output speed

51 tokens/s

Faster than average, making it suitable for high-throughput generation tasks.

Input price

$2.00 /M tokens

Significantly above average for input processing, impacting costs for large contexts.

Output price

$8.00 /M tokens

One of the highest output token costs observed, quickly escalating expenses for verbose outputs.

Verbosity signal

N/A

Data not available for this metric, making it difficult to assess its natural verbosity.

Provider latency

0.85 seconds

Moderate time to first token, which might impact real-time interactive applications.

Technical specifications

Spec	Details
Model Name	Jamba 1.6 Large
Owner	AI21 Labs
License	Open
Model Type	Non-Reasoning, Open-Weight
Input Modality	Text
Output Modality	Text
Context Window	256k tokens
Intelligence Index	14 (Rank 28/30)
Median Output Speed	51 tokens/s
Latency (TTFT)	0.85 seconds
Input Price	$2.00 / 1M tokens
Output Price	$8.00 / 1M tokens
Blended Price (3:1)	$3.50 / 1M tokens
API Provider	AI21 Labs

What stands out beyond the scoreboard

Where this model wins

Massive Context Window: With 256k tokens, Jamba 1.6 Large excels at processing and generating content based on extremely long documents, entire books, or extended conversational histories.
Above-Average Output Speed: At 51 tokens per second, it can generate responses quickly, which is beneficial for applications requiring high-volume text generation or rapid content creation.
Open-Weight Model: Its open-weight nature provides significant flexibility for developers to fine-tune the model for specific domains, tasks, or to deploy it in custom environments, offering greater control and customization.
Reliable Text-to-Text Generation: For standard language tasks like summarization, translation, or creative writing where complex reasoning is not the primary requirement, it performs consistently.

Where costs sneak up

High Input Token Price: At $2.00 per 1M input tokens, processing large context windows, which is one of its strengths, can quickly become very expensive, eroding its value proposition for extensive inputs.
Very High Output Token Price: The $8.00 per 1M output tokens is among the highest observed, meaning that any verbose output or extensive generation will significantly drive up operational costs.
Low Intelligence Score: Its low ranking on the intelligence index (14/100) suggests it may struggle with nuanced, complex, or reasoning-intensive tasks, potentially requiring more iterations or human oversight, increasing overall project costs.
Blended Price Impact: Even with a 3:1 input-to-output blend, the $3.50 per 1M tokens is still on the higher side, indicating that cost-efficiency is not its primary advantage across typical workloads.
Non-Reasoning Limitations: For applications demanding deep understanding, logical inference, or problem-solving, Jamba 1.6 Large's performance might necessitate fallback to more capable (and often more expensive) models, or significant prompt engineering.

Provider pick

Jamba 1.6 Large is exclusively offered by AI21 Labs, the model's developer. This means that direct access and consistent performance metrics are tied to their API.

While this simplifies provider selection, it also means there are no alternative providers to compare for pricing or specific service level agreements.

Priority	Pick	Why	Tradeoff to accept
Primary	AI21 Labs	Direct access to the model, consistent performance as benchmarked.	No alternative providers for competitive pricing or redundancy.

Note: Jamba 1.6 Large is currently available exclusively through AI21 Labs.

Real workloads cost table

Understanding the real-world cost implications of Jamba 1.6 Large requires looking beyond per-token prices and considering typical usage patterns. The following scenarios illustrate estimated costs for common AI tasks, highlighting how its pricing structure impacts different workloads.

These estimates use the benchmarked prices of $2.00 per 1M input tokens and $8.00 per 1M output tokens.

Scenario	Input	Output	What it represents	Estimated cost
Summarizing a Long Document	100,000 tokens	5,000 tokens	Condensing a detailed report or research paper.	$0.20 (input) + $0.04 (output) = $0.24
Extended Chatbot Interaction	2,000 tokens (per turn, 10 turns)	1,000 tokens (per turn, 10 turns)	A user engaging in a lengthy conversation with a virtual assistant.	$0.04 (input) + $0.08 (output) = $0.12
Creative Content Generation	5,000 tokens	20,000 tokens	Generating a blog post or marketing copy from a brief.	$0.01 (input) + $0.16 (output) = $0.17
Data Extraction from Text	50,000 tokens	2,000 tokens	Extracting key information from a collection of emails or articles.	$0.10 (input) + $0.016 (output) = $0.116
Code Generation (Small Function)	1,000 tokens	500 tokens	Generating a simple utility function based on a prompt.	$0.002 (input) + $0.004 (output) = $0.006
Translation of a Medium Article	10,000 tokens	10,000 tokens	Translating a typical blog post from one language to another.	$0.02 (input) + $0.08 (output) = $0.10

These examples demonstrate that while Jamba 1.6 Large's input costs can be significant for very large contexts, its high output token price is the primary driver of expense for most generative tasks. Applications requiring extensive or verbose outputs will incur substantial costs, making careful output management crucial.

How to control cost (a practical playbook)

Optimizing costs when using Jamba 1.6 Large requires a strategic approach, particularly given its higher-than-average pricing. Focusing on efficient prompt engineering and output management can significantly mitigate expenses.

Here are key strategies to maximize value and control costs:

Optimize Prompt Length

Given the $2.00/M input token price, every token in your prompt contributes to the cost. While Jamba 1.6 Large has a massive context window, using it judiciously is key.

Be Concise: Remove unnecessary words, examples, or instructions from your prompts.
Reference, Don't Embed: Instead of embedding entire documents, provide key excerpts or summaries if the full context isn't strictly necessary for every call.
Iterative Prompting: Break down complex tasks into smaller steps, feeding only relevant context for each step rather than the entire document repeatedly.

Control Output Verbosity

The $8.00/M output token price is a major cost factor. Controlling the length and detail of the model's responses is paramount.

Specify Length: Explicitly instruct the model to be concise, provide bullet points, or limit its output to a certain number of words or sentences.
Structured Output: Request JSON or other structured formats that inherently limit verbosity compared to free-form text.
Post-Processing: If the model tends to be verbose, consider a lightweight post-processing step to trim or summarize its output before final use.

Leverage Speed for Throughput

Jamba 1.6 Large's above-average output speed can be an advantage for high-volume, non-interactive tasks.

Batch Processing: For tasks like document summarization or content generation, process multiple requests in batches to maximize efficiency and minimize overhead.
Asynchronous Operations: Design your application to handle responses asynchronously, allowing the model to work through a queue of requests without blocking user interactions.

Match Task to Model Capabilities

Given its lower intelligence score, Jamba 1.6 Large is best suited for specific types of tasks.

Content Generation: Ideal for creative writing, drafting, or expanding on ideas where the quality of the output is more about fluency than deep reasoning.
Summarization/Extraction (Simple): Effective for extracting facts or summarizing content that doesn't require complex inference.
Long Context Handling: Utilize its 256k context window for tasks where the sheer volume of input data is the primary challenge, rather than intricate analysis.

Monitor Usage and Costs

Regularly track your API usage and associated costs to identify patterns and areas for optimization.

Set Budgets and Alerts: Implement spending limits and notifications through your AI21 Labs account to prevent unexpected cost overruns.
Analyze Token Counts: Review the input and output token counts for your typical requests to understand where the majority of your costs are accumulating.
A/B Test Prompts: Experiment with different prompt structures and lengths to find the most cost-effective way to achieve desired results.

FAQ

What is Jamba 1.6 Large?

Jamba 1.6 Large is an open-weight, non-reasoning large language model developed by AI21 Labs. It is designed for text-to-text generation and is notable for its exceptionally large 256k token context window.

Who owns Jamba 1.6 Large?

Jamba 1.6 Large is owned and developed by AI21 Labs.

What are its main strengths?

Its primary strengths include a massive 256k token context window, allowing it to process very long inputs, and an above-average output speed of 51 tokens per second. Being an open-weight model also offers flexibility for custom deployments and fine-tuning.

What are its main weaknesses?

Jamba 1.6 Large scores low on intelligence (14/100), indicating limited reasoning capabilities. It is also particularly expensive, with high input ($2.00/M tokens) and very high output ($8.00/M tokens) pricing, making cost management a significant concern.

What is its context window size?

Jamba 1.6 Large features an impressive 256,000 token context window, enabling it to handle extensive amounts of information in a single prompt.

How does its pricing compare to other models?

Jamba 1.6 Large is considered expensive. Its input token price of $2.00 per 1M tokens is significantly above average, and its output token price of $8.00 per 1M tokens is among the highest benchmarked, leading to a high blended cost of $3.50 per 1M tokens.

Is it suitable for complex reasoning tasks?

No, Jamba 1.6 Large is classified as a non-reasoning model and scores low on intelligence metrics. It is not recommended for tasks requiring complex logical inference, deep understanding, or nuanced problem-solving.

What is its output speed?

Jamba 1.6 Large has a median output speed of 51 tokens per second, which is faster than the average for comparable models, making it efficient for generating text quickly.

Jamba 1.6 Large (non-reasoning)