Jamba 1.5 Large (non-reasoning)

Jamba 1.5 Large excels at processing extremely long inputs. Use this to your advantage for tasks like summarizing entire books, analyzing extensive legal documents, or processing large codebases in a single call, minimizing API call overhead.

• Consolidate multiple smaller prompts into one larger, more comprehensive request to reduce the number of API calls.
• Ensure your input data is well-structured and relevant to maximize the utility of the large context, avoiding unnecessary token consumption.

Given its lower intelligence score, Jamba 1.5 Large performs best with clear, direct instructions. Avoid complex reasoning chains or highly abstract requests that might lead to suboptimal or verbose outputs, increasing costs.

• Break down complex tasks into simpler, sequential steps if necessary, processing each step with a focused prompt.
• Use explicit formatting instructions for desired output (e.g., "Return as JSON," "Summarize in 3 bullet points") to control output token count.

Output tokens are significantly more expensive than input tokens. Actively manage the length and detail of the model's responses to prevent unnecessary expenditure.

• Implement strict output length constraints in your prompts (e.g., "Limit summary to 200 words," "Provide only the answer, no preamble").
• Post-process model outputs to trim unnecessary boilerplate or redundant information before storing or displaying.

For tasks involving many similar, independent requests, consider batching them into a single API call if the total context fits within the 256k limit. This can reduce per-request overhead and improve throughput.

• Design your application to aggregate data for processing, sending larger chunks less frequently.
• Be mindful of the model's intelligence; batching too many disparate or complex tasks might dilute its focus and lead to less accurate results.

While pricing is similar across Amazon Bedrock and Google Vertex, minor performance differences exist. If your application is highly sensitive to latency or throughput, choose the provider that offers the best performance for your region and specific workload.

• Benchmark both Amazon Bedrock and Google Vertex with your actual data to identify the marginal performance leader for your specific use case.
• Consider your existing cloud infrastructure to minimize data transfer costs and simplify integration efforts.

Jamba 1.5 Large is an open-licensed large language model developed by AI21 Labs. It is distinguished by its exceptionally large 256k token context window, making it suitable for processing vast amounts of text in a single interaction.

It scores 15 on the Artificial Analysis Intelligence Index, placing it among the lower-performing models in its class (average is 33). This means it is less suited for complex reasoning, nuanced understanding, or highly creative tasks compared to more intelligent models.

Yes, Jamba 1.5 Large is considered expensive. Its input token price of $2.00 per 1M tokens and output token price of $8.00 per 1M tokens are significantly higher than the market averages of $0.56 and $1.67, respectively.

Its primary strength lies in processing and extracting information from extremely long documents, such as legal briefs, research papers, or extensive reports. It's ideal for tasks where the volume of text is high and the required intelligence level is moderate, like summarization, data extraction, or content rephrasing.

Amazon Bedrock generally offers slightly better performance in terms of output speed (46 t/s) and latency (0.56s) compared to Google Vertex (41 t/s and 0.57s). Since pricing is identical across these providers, Amazon Bedrock is often the preferred choice for performance-sensitive applications.

Jamba 1.5 Large has knowledge up to March 2024, meaning it can draw upon information and events up to that date for its responses.

While it can generate text, its lower intelligence score means it may struggle with highly creative writing, complex problem-solving, or tasks requiring deep understanding and nuanced reasoning. For such applications, models with higher intelligence scores would be more suitable.