Jamba 1.7 Mini offers exceptional speed and conciseness at a competitive price, making it ideal for high-throughput, low-intelligence tasks.
Jamba 1.7 Mini emerges as a compelling option for applications prioritizing speed and cost-efficiency over complex reasoning. Positioned as a non-reasoning model, it delivers remarkable performance in output speed and conciseness, making it a strong contender for high-volume, straightforward text generation and processing tasks. Its open license further enhances its appeal, allowing for broad integration and experimentation across various platforms.
While its intelligence score places it below average among comparable models, Jamba 1.7 Mini compensates with its highly optimized output. It scored 15 on the Artificial Analysis Intelligence Index, where the average is 22. Crucially, during this evaluation, it generated a mere 4.4 million tokens, significantly less than the average of 8.5 million, highlighting its exceptional conciseness. This efficiency directly translates into lower operational costs, especially for applications where token count is a primary cost driver.
From a pricing perspective, Jamba 1.7 Mini offers competitive rates. Input tokens are priced at $0.20 per 1 million tokens, aligning with the market average. Output tokens are slightly higher at $0.40 per 1 million tokens, still moderately priced compared to an average of $0.54. The blended price, based on a 3:1 input-to-output ratio, stands at an attractive $0.25 per 1 million tokens. The total cost to evaluate Jamba 1.7 Mini on the Intelligence Index was $20.89, underscoring its cost-effectiveness for extensive testing.
Speed is another area where Jamba 1.7 Mini truly shines. With a median output speed of 152 tokens per second, it ranks among the fastest models available, ensuring rapid response times for demanding applications. Its latency, or time to first token (TTFT), is also impressive at 0.58 seconds. Coupled with a substantial 258k token context window and knowledge up to August 2024, Jamba 1.7 Mini is well-equipped to handle large inputs and deliver quick, concise outputs for a wide array of text-based applications.
15 (#20 / 33 / 33)
152 tokens/s
$0.20 per 1M tokens
$0.40 per 1M tokens
4.4M tokens
0.58 seconds
| Spec | Details |
|---|---|
| Model Name | Jamba 1.7 Mini |
| Owner | AI21 Labs |
| License | Open |
| Context Window | 258k tokens |
| Knowledge Cutoff | August 2024 |
| Input Type | Text |
| Output Type | Text |
| Intelligence Index Score | 15 (out of 33) |
| Output Speed (median) | 152 tokens/s |
| Latency (TTFT) | 0.58 seconds |
| Input Price | $0.20 / 1M tokens |
| Output Price | $0.40 / 1M tokens |
| Blended Price (3:1) | $0.25 / 1M tokens |
| Verbosity (Intelligence Index) | 4.4M tokens |
Jamba 1.7 Mini is currently benchmarked exclusively through AI21 Labs, which serves as the primary and direct provider for this model. This simplifies the provider selection process but also means that users will primarily interact with AI21 Labs' infrastructure and pricing structure.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Priority | Pick | Why | Tradeoff |
| Performance & Direct Access | AI21 Labs | As the model owner and sole benchmarked provider, AI21 Labs offers direct access to Jamba 1.7 Mini, ensuring optimized performance and integration with their ecosystem. | Limited provider choice means no alternative pricing or infrastructure options to compare against. |
| Cost-Efficiency (Current) | AI21 Labs | Jamba 1.7 Mini's competitive pricing and high conciseness through AI21 Labs make it a cost-effective choice for suitable workloads. | Without other providers, there's no competitive pressure to drive prices lower or offer alternative pricing models. |
| Ease of Integration | AI21 Labs | Leveraging AI21 Labs' existing APIs and documentation for Jamba 1.7 Mini can streamline integration into existing or new applications. | Reliance on a single vendor's API structure and terms of service. |
Note: Provider recommendations are based on current benchmark data and available information. As the market evolves, additional providers or deployment options may become available.
Understanding the real-world cost implications of Jamba 1.7 Mini requires looking beyond per-token rates and considering typical usage scenarios. Its high speed and conciseness can significantly impact overall expenditure, especially for high-volume applications.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input (tokens) | Output (tokens) | What it represents | Estimated Cost (per 1M operations) |
| Short Q&A / Chatbot Response | 1,000 | 100 | Processing a user query and generating a concise answer. | $240.00 |
| Document Summarization | 100,000 | 5,000 | Summarizing a long article or report into key points. | $2,200.00 |
| Content Generation (Short Form) | 500 | 2,000 | Generating short social media posts, product descriptions, or email snippets. | $900.00 |
| Data Extraction (Structured) | 5,000 | 500 | Extracting specific entities or structured data from a larger text block. | $120.00 |
| Code Commenting / Doc Generation | 10,000 | 1,000 | Adding comments to code or generating basic documentation sections. | $240.00 |
Jamba 1.7 Mini's cost-effectiveness shines in scenarios requiring high throughput and concise outputs. Its competitive pricing, combined with its ability to generate fewer tokens, makes it particularly attractive for applications like data extraction and short-form content generation where volume is high and intelligence requirements are moderate.
Optimizing costs with Jamba 1.7 Mini involves leveraging its strengths – speed and conciseness – while being mindful of its intelligence limitations and context window usage. Here are key strategies to maximize efficiency:
Jamba 1.7 Mini's standout feature is its ability to produce highly concise outputs. This directly translates to lower output token costs, which are typically higher than input token costs.
With a large 258k context window, Jamba 1.7 Mini can handle extensive inputs. However, filling this window unnecessarily can quickly accumulate input token costs.
Jamba 1.7 Mini's high output speed makes it an excellent candidate for batch processing, which can improve overall system efficiency and potentially reduce per-request overheads.
Continuous monitoring of token usage is crucial for identifying cost-saving opportunities and ensuring that your applications are running efficiently.
Jamba 1.7 Mini excels in high-throughput applications that require fast, concise text generation and processing, but do not demand complex reasoning. This includes tasks like short-form content generation, data extraction, summarization of factual texts, and chatbot responses where direct answers are preferred.
It scores 15 on the Artificial Analysis Intelligence Index, placing it below the average of 22 for comparable models. This indicates it's not designed for advanced reasoning, complex problem-solving, or highly nuanced understanding. Its strength lies in efficient execution of more straightforward tasks.
Jamba 1.7 Mini features a substantial 258k token context window. This allows it to process very large input documents or extensive conversational histories, making it suitable for applications requiring a broad understanding of the provided context.
Yes, it is considered cost-effective, especially due to its competitive pricing ($0.20/M input, $0.40/M output) and its exceptional conciseness. By generating fewer tokens for its outputs, it helps reduce overall expenditure, making it a strong choice for budget-conscious, high-volume operations.
Jamba 1.7 Mini is notably fast, achieving a median output speed of 152 tokens per second. Its time to first token (latency) is also quick at 0.58 seconds, ensuring rapid responses and efficient processing for real-time applications.
Jamba 1.7 Mini is available under an Open license. This provides users with significant flexibility for integration, deployment, and experimentation across various platforms and use cases without restrictive proprietary licensing terms.
The model's knowledge base extends up to August 2024. This means it has been trained on data available up to that point and may not have information on events or developments occurring after this date.