AI21 Labs' hybrid MoE model, offering a massive 256k context window but with lower intelligence scores and high pricing.
Jamba 1.5 Large, developed by AI21 Labs, represents a significant architectural experiment in the landscape of large language models. It diverges from the ubiquitous Transformer-only design by implementing a novel hybrid architecture that blends elements of a State Space Model (Mamba) with traditional Transformer blocks. This Jamba-Specific Mixture-of-Experts (JSM) approach aims to capture the best of both worlds: the efficiency and long-context handling of Mamba and the proven reasoning and instruction-following capabilities of Transformers. The model features a massive 256,000-token context window, positioning it as a potential powerhouse for tasks involving extremely long documents, such as legal analysis, financial reporting, or comprehensive literature reviews.
Despite its innovative design and impressive context length, Jamba 1.5 Large struggles significantly in terms of general intelligence and reasoning. On the Artificial Analysis Intelligence Index, it scores a mere 15, placing it near the bottom of the rankings and well below the average of 33 for comparable models. This low score indicates deficiencies in complex problem-solving, nuanced instruction following, and creative generation tasks. Consequently, Jamba is not a general-purpose workhorse model in the vein of a GPT-4 or Claude 3. Its strengths are highly specialized, and it is likely to underperform on tasks that require a deep understanding of logic, causality, or abstract concepts.
The model's value proposition is further complicated by its pricing structure. With input tokens at $2.00 per million and output tokens at a steep $8.00 per million, Jamba is exceptionally expensive for an open-weight model. These prices are multiples higher than the class averages, which hover around $0.56 for input and $1.67 for output. This high cost creates a challenging dynamic: the model's primary feature, its 256k context window, becomes prohibitively expensive to use at scale. A single prompt that fully utilizes the context window could cost over $0.50, a price at which users would typically expect state-of-the-art intelligence. This positions Jamba 1.5 Large as a niche tool, best suited for specific, well-defined problems where its unique long-context ability is a hard requirement and its cognitive limitations are not a blocker.
Available through major cloud providers like Amazon Bedrock and Google Vertex AI, Jamba 1.5 Large offers enterprise-grade accessibility and performance. Benchmarks show very competitive latency (Time To First Token) of around 0.56 seconds and solid output speeds of up to 46 tokens per second. This responsiveness makes it viable for interactive applications, provided the use case fits its narrow profile. Developers considering Jamba must carefully weigh its unique architectural benefits and massive context against its significant drawbacks in intelligence and cost. It is a tool for specialists, not a generalist, and requires a clear understanding of its trade-offs to be deployed effectively.
15 (26 / 30)
46 tokens/s
$2.00 / 1M tokens
$8.00 / 1M tokens
N/A
0.56 s
| Spec | Details |
|---|---|
| Model Owner | AI21 Labs |
| Architecture | Hybrid SSM-Transformer (Jamba) |
| Mixture-of-Experts (MoE) | Yes, 12B active parameters out of 52B total |
| Context Window | 256,000 tokens |
| Knowledge Cutoff | March 2024 |
| License | Apache 2.0 |
| Model Type | Open Weights |
| Primary Language | English |
| Intended Use | Long-context summarization, RAG, and analysis |
| API Providers | Amazon Bedrock, Google Vertex AI |
Choosing a provider for Jamba 1.5 Large is straightforward, as the two main options, Amazon Bedrock and Google Vertex AI, offer nearly identical performance and pricing. The decision will likely come down to minor performance differences or existing platform preferences.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Price | Tie (Amazon / Google) | Both providers offer identical pricing at $2.00 per 1M input tokens and $8.00 per 1M output tokens. | There is no price-related tradeoff between these providers. |
| Fastest Output Speed | Amazon Bedrock | At 46 tokens/second, Bedrock demonstrates a ~12% speed advantage over Google Vertex (41 t/s) in our benchmarks. | This difference, while measurable, may not be noticeable in many real-world applications. |
| Lowest Latency (TTFT) | Amazon Bedrock | Bedrock has a marginal edge with a time-to-first-token of 0.56s, compared to Google's 0.57s. | A difference of 0.01 seconds is imperceptible to human users. |
| Overall Pick | Amazon Bedrock | With identical pricing, Bedrock's slight, consistent advantages in both output speed and latency make it the marginally better choice. | The performance gains are small, so users heavily invested in the Google Cloud ecosystem may find Vertex to be a more convenient choice. |
Performance metrics are based on benchmarks conducted by Artificial Analysis and represent a snapshot in time. Provider performance can vary based on region, load, and API updates. Prices are subject to change.
Jamba 1.5 Large's cost profile is defined by its expensive tokens. The following scenarios illustrate how costs accrue, particularly when leveraging its primary feature: the large context window. Note how quickly the price increases when processing large documents.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Simple Chatbot Query | 2,000 tokens | 250 tokens | A standard user interaction with some conversational history. | ~$0.006 |
| Code Generation Task | 500 tokens | 1,000 tokens | Generating a function or small script based on a description. | ~$0.009 |
| Summarize a 50-Page Report | 25,000 tokens | 500 tokens | A common RAG or summarization task on a medium-length document. | ~$0.054 |
| Analyze a Long Legal Document | 150,000 tokens | 2,000 tokens | A task that begins to leverage the model's unique context length. | ~$0.316 |
| Full Context Window Analysis | 250,000 tokens | 2,000 tokens | Maxing out the context to find details in a massive research paper or book. | ~$0.516 |
The takeaway is clear: while simple tasks are still measured in fractions of a cent, the cost of using Jamba's primary feature—its large context—is substantial. A single query can cost over half a dollar, a price that demands a clear and valuable return on investment that Jamba's low intelligence may not be able to provide.
Given Jamba 1.5 Large's high token costs and specific strengths, managing expenses is crucial. A deliberate strategy is required to extract value without incurring excessive fees. Focus on aligning your use case with the model's unique profile and minimizing expensive operations.
Do not use Jamba as a general-purpose model. Its cost and low intelligence make it unsuitable for that role. Instead, focus exclusively on tasks where its 256k context window is a hard requirement and its reasoning limitations are acceptable.
Output tokens are four times more expensive than input tokens. This pricing structure heavily penalizes verbose responses. Engineer your prompts to force the model to be as concise as possible.
Before committing to Jamba, rigorously test your use case against other models. A model with a smaller context window but higher intelligence might solve your problem more effectively and cheaply through better summarization or chaining.
As an open-weight model, Jamba can be self-hosted. This path involves significant upfront investment in powerful GPU infrastructure and ML operations expertise. However, for extremely high-volume, long-context workloads, it can eventually become more cost-effective than paying per-token API fees.
Jamba combines two different types of neural network layers. Transformer layers are excellent at reasoning and understanding complex instructions. State Space Model (SSM) layers, specifically Mamba in this case, are highly efficient at processing very long sequences of data. By alternating these layers, AI21 Labs aims to create a model that can handle a huge context window efficiently while retaining some of the reasoning power of a traditional Transformer.
Jamba is a Mixture-of-Experts (MoE) model with a total of 52 billion parameters. However, for any given token it processes, it only uses a fraction of them—about 12 billion. It has multiple 'expert' sub-networks and a routing system that selects the most relevant experts for the task at hand. This makes inference much faster and less computationally expensive than running a dense 52B parameter model, while theoretically retaining the knowledge capacity of the larger model.
Several factors contribute to its high price. First, it is a large model (52B total parameters) with a novel architecture, which can be costly to serve. Second, its massive 256k context window requires significant memory resources on the provider's side. Finally, the API providers (Amazon and Google) set prices based on their operational costs and the model's perceived market value as a specialized tool for long-context applications.
Its primary strength is in processing single, extremely long documents for tasks that do not require deep reasoning. Good use cases include:
Jamba is not a direct competitor in terms of general intelligence. Models like GPT-4 and Claude 3 Sonnet are vastly superior at reasoning, instruction following, and creative tasks. Jamba's key differentiator is its open, hybrid architecture and its ability to handle a 256k context window, whereas its cognitive abilities are significantly weaker. It is a specialized tool, not a general-purpose cognitive engine.
While technically possible, using the full 256k context window is often impractical due to cost. As shown in the 'Real World Workloads' section, a single prompt using most of the context can cost over $0.50. Therefore, its use should be reserved for high-value tasks where no other method (like document chunking with a smarter model) is feasible and the cost is justified.