Jamba 1.7 Mini (non-reasoning)

An exceptionally fast and concise model for specialized tasks.

Jamba 1.7 Mini (non-reasoning)

An exceptionally fast and concise open-weight model from AI21 Labs, offering a massive context window at a competitive price point for non-reasoning tasks.

Open Weight258k ContextHigh SpeedConcise OutputAI21 Labs

Jamba 1.7 Mini is a compact, open-weight model from AI21 Labs that carves out a unique niche in the AI landscape. It is built on the innovative Jamba architecture, a hybrid that strategically combines the strengths of traditional Transformers with the efficiency of Mamba-style State Space Models (SSMs). This design choice is not merely academic; it directly translates into the model's distinct performance profile, prioritizing exceptional speed and a massive context window over the complex reasoning abilities found in larger, more generalist models.

The most striking characteristics of Jamba 1.7 Mini are its velocity and brevity. Clocking in at a median output speed of 152 tokens per second, it is one of the fastest models available, more than doubling the average speed of its peers. This makes it an ideal candidate for applications where real-time interaction and low latency are paramount, such as interactive chatbots, live data processing, and content classification. Complementing its speed is its remarkable conciseness. In our benchmark testing, it generated only 4.4 million tokens where the average model produces 8.5 million. This tendency towards brevity not only means users get to the point faster but also directly translates into lower costs for output tokens, a significant advantage for high-volume applications.

However, this performance comes with a clear and important trade-off: intelligence. With a score of 15 on the Artificial Analysis Intelligence Index, Jamba 1.7 Mini falls notably below the class average of 22. It is not designed for tasks requiring deep reasoning, nuanced understanding, creative generation, or complex multi-step instruction following. Attempting to use it as a general-purpose creative partner or a sophisticated problem-solver will lead to frustration. Instead, it should be viewed as a highly specialized tool, optimized for speed and efficiency on well-defined, lower-complexity tasks.

Where Jamba 1.7 Mini truly redefines expectations is with its colossal 258,000-token context window. This is an extraordinary capacity for a model of its size and cost, enabling it to process and analyze vast amounts of information in a single pass. This feature unlocks powerful use cases, from summarizing entire books and lengthy research papers to building sophisticated Retrieval-Augmented Generation (RAG) systems that can query extensive knowledge bases without complex chunking strategies. When combined with its competitive pricing, Jamba 1.7 Mini presents a compelling, cost-effective solution for context-heavy workloads that do not demand high-level cognitive abilities.

Scoreboard

Intelligence

15 (20 / 33)

Scores below average on the Intelligence Index, making it less suitable for complex reasoning or nuanced generation.

Output speed

152 tokens/s

Extremely fast, ranking among the top performers and delivering near-instantaneous responses for many use cases.

Input price

$0.20 / 1M tokens

Competitively priced for input, matching the average for models in its performance class.

Output price

$0.40 / 1M tokens

Priced favorably for output, coming in significantly below the average for its peers.

Verbosity signal

4.4M tokens

Highly concise, generating roughly half the tokens of an average model on the same benchmark tasks.

Provider latency

0.58 seconds

Quick to respond, with a low time-to-first-token that enhances the user experience in interactive applications.

Technical specifications

Spec	Details
Owner	AI21 Labs
License	Open (Apache 2.0)
Architecture	Jamba (Hybrid SSM-Transformer)
Context Window	258,000 tokens
Knowledge Cutoff	August 2024
Input Modality	Text
Output Modality	Text
Input Price	$0.20 / 1M tokens
Output Price	$0.40 / 1M tokens
Blended Price (3:1)	$0.25 / 1M tokens
API Provider	AI21 Labs

What stands out beyond the scoreboard

Where this model wins

Blazing Speed: With a median output of 152 tokens/second, it's perfect for real-time, interactive applications where latency is a critical factor.
Extreme Conciseness: Its tendency to produce short, to-the-point answers reduces output token costs and total response time, making it highly efficient for high-volume tasks.
Massive Context Window: The 258k context window is exceptional for a model this size, enabling analysis of long documents, reports, and transcripts in a single prompt.
Cost-Effective Specialist: For tasks like summarization, classification, and simple RAG, its combination of low price, high speed, and large context offers unparalleled value.
Predictable Output: As a smaller, non-reasoning model, its outputs for simple, well-defined tasks are often more consistent and less prone to unexpected creativity or verbosity.

Where costs sneak up

Low Intelligence Requires Reruns: For any query that strays into moderate complexity, the model may fail, requiring multiple attempts and prompt adjustments that erode its initial cost savings.
Unsuitable for Complex Work: Using it for tasks it's not built for—like creative writing, coding, or nuanced analysis—will waste tokens, time, and development effort.
The Large Context Trap: While the 258k context window is a powerful feature, filling it can become expensive. A single full-context prompt costs over $50 in input tokens alone.
Over-Summarization Risk: Its extreme conciseness can be a double-edged sword, sometimes leading to summaries that strip out crucial nuance or detail.
Single Provider: Being available only through AI21 Labs means there is no competition on price or performance, and users are dependent on a single vendor's infrastructure.

Provider pick

Jamba 1.7 Mini is currently available exclusively through its creator, AI21 Labs. This makes the choice of provider straightforward, but it's still useful to analyze how it fits different priorities, as the primary trade-offs are inherent to the model itself.

Priority	Pick	Why	Tradeoff to accept
Lowest Cost	AI21 Labs	As the sole provider, AI21 Labs offers competitive pricing for this model class, especially given its speed and context length.	The model's low intelligence is the main tradeoff for its low cost.
Highest Speed	AI21 Labs	The model's hybrid architecture is specifically designed for speed, and AI21's optimized hosting delivers top-tier performance with low latency.	You sacrifice reasoning ability and nuance for raw output velocity.
Best for Large Context	AI21 Labs	The 258k context window is a key feature, and AI21 Labs is the only place to access it for this model.	The cost of utilizing the full context window can be substantial if not managed carefully.
Best Overall	AI21 Labs	It's the only choice, offering a unique package of speed, conciseness, and a large context window that is unmatched for specific use cases.	It is a specialist model, not a general-purpose one, limiting its overall utility.

Provider analysis is based on benchmark data collected by Artificial Analysis. Since AI21 Labs is the sole provider for Jamba 1.7 Mini, all performance metrics reflect their specific implementation.

Real workloads cost table

To understand the practical cost of using Jamba 1.7 Mini, let's estimate its performance on several real-world scenarios where its strengths—speed, conciseness, and large context—are most relevant. These examples highlight its cost-effectiveness for specific, high-volume tasks.

Scenario	Input	Output	What it represents	Estimated cost
Summarize a long report	25,000 tokens	500 tokens	Processing a lengthy document to extract key insights, leveraging the large context window.	~$0.0052
RAG-based support bot	12,000 tokens (context) + 150 (query)	250 tokens	Answering a user question by searching a provided knowledge base.	~$0.0025
Extract entities from an article	1,500 tokens	50 tokens	A common data extraction task, perfect for its speed and low cost.	~$0.00032
Classify user sentiment	200 tokens	10 tokens	A high-volume, low-complexity task ideal for a fast, cheap model.	~$0.000044
Maintain a long chat history	50,000 tokens (history) + 200 (new)	300 tokens	Keeping track of a long conversation to provide contextually aware responses.	~$0.01016

The takeaway is clear: Jamba 1.7 Mini is exceptionally cheap for tasks that are either context-heavy (but simple) or high-volume and repetitive. Costs are measured in fractions of a cent, making it feasible to deploy at a massive scale for things like classification, simple Q&A, and data extraction.

How to control cost (a practical playbook)

To maximize the value of Jamba 1.7 Mini, it's crucial to adopt a strategy that plays to its unique strengths while mitigating its weaknesses. This isn't a one-size-fits-all model; it's a precision tool. The following strategies will help you integrate it effectively and control costs.

Use a Model Cascade

The most effective way to use Jamba 1.7 Mini is as the first line of defense in a model cascade. Its speed and low cost make it perfect for handling the vast majority of simple, high-volume requests.

Initial Triage: Route all incoming requests to Jamba 1.7 Mini first. Use it for simple tasks like intent classification, sentiment analysis, or basic data extraction.
Escalation Path: If Jamba 1.7 Mini's output is insufficient or if the initial request is identified as complex, automatically escalate the task to a more intelligent (and expensive) model like GPT-4o or Claude 3 Opus.
Cost Savings: This approach ensures you only pay for expensive reasoning capabilities when you absolutely need them, dramatically lowering the average cost per request across your application.

Master the Massive Context Window

The 258k context window is a defining feature. Use it for tasks that were previously impractical or required complex engineering with smaller-context models.

"Needle in a Haystack": Feed entire documents, codebases, or transcripts into the prompt and ask the model to find specific pieces of information. Its speed makes this surprisingly fast.
Simplified RAG: For many use cases, you can bypass complex document chunking and vectorization. Simply provide the raw text of a few large documents as context for Q&A tasks.
Cost Awareness: Be mindful of the cost. While the per-token price is low, filling the context window is not free. A full 258k context prompt costs over $50. Use it when necessary, not by default.

Lean into Conciseness for Efficiency

Jamba 1.7 Mini's tendency to be brief is a feature, not a bug. Leverage it to reduce costs and improve user experience.

Prompt for Brevity: Reinforce its natural tendency by including instructions like "Be concise," "Answer in one sentence," or "Provide a bulleted list."
Ideal Use Cases: This makes it perfect for generating headlines, meta descriptions, keywords, or short summaries where verbosity is undesirable.
Lower Output Costs: Since output tokens are twice as expensive as input tokens, its conciseness directly translates to significant cost savings at scale.

FAQ

What is Jamba 1.7 Mini?

Jamba 1.7 Mini is a small, open-weight language model developed by AI21 Labs. It is notable for its hybrid architecture, which combines elements of Transformers and State Space Models (Mamba) to achieve very high processing speeds, a large context window, and high efficiency, at the cost of lower reasoning ability compared to larger models.

What is a hybrid SSM-Transformer architecture?

It's a novel model design that aims to get the best of both worlds. Transformers are excellent at reasoning and understanding complex relationships in data, but can be slow and memory-intensive, especially with long contexts. State Space Models (SSMs) like Mamba are extremely fast and efficient at processing long sequences. The Jamba architecture uses a mix of both types of layers, allowing it to process vast amounts of context efficiently while retaining sufficient language capabilities for specific tasks.

What tasks is Jamba 1.7 Mini best suited for?

Jamba 1.7 Mini excels at tasks that require speed, a large context, and low cost, but not deep reasoning. Top use cases include:

Real-time chatbots and conversational agents.
High-volume text classification, sentiment analysis, and entity extraction.
Summarizing very long documents, transcripts, or books.
Retrieval-Augmented Generation (RAG) over large, provided texts.
Data formatting and simple transformations.

What are the main limitations of this model?

The primary limitation is its low intelligence score. It is not suitable for complex problem-solving, creative writing, programming, or following nuanced, multi-step instructions. Its outputs can be simplistic, and it may fail to grasp subtle context or intent. It should be used as a specialist tool, not a general-purpose AI assistant.

How does its 258k context window compare to other models?

A 258,000-token context window is exceptionally large, especially for a model of this size and cost. It is larger than standard versions of many flagship models like GPT-4 Turbo (128k) and is competitive with the largest context windows available on the market (e.g., Claude 3's 200k, Gemini 1.5 Pro's 1M). This makes it a standout choice for applications that need to process long-form content.

Is "Jamba" related to "Mamba"?

Yes. "Mamba" refers to a specific State Space Model (SSM) architecture that gained prominence for its efficiency with long sequences. "Jamba" is the name AI21 Labs gave to their hybrid architecture that explicitly incorporates Mamba-style layers alongside traditional Transformer layers. The name is a direct nod to one of its core technological components.

Jamba 1.7 Mini (non-reasoning)