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Jamba Large 1.6

Jamba Large 1.6 is the ultimate enterprise AI model, delivering unmatched speed at 61 tokens per second, a massive 256K context window, and superior performance on RAG, long-context QA, and benchmarks over rivals like Mistral, Meta, and Cohere. Deploy it privately on-prem or in-VPC for secure, efficient handling of complex data workflows without compromising accuracy or control.

256k Context
Low Intelligence
Mar '24 Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU-Pro
50.7%
GPQA
71.2%
MMLU
65.3%

How do you want to interact?

Start a Conversation

Ask anything.
Have a natural conversation, brainstorm ideas, draft emails, or ask for advice.

Start Chatting

Use a Persona

Specialized Experts.
Instruct the AI to act as a Coding Tutor, Marketing Expert, or Travel Guide.

Pick a Persona

Why use Jamba Large 1.6?

Long-Context Processing

Handles up to 256K tokens with superior efficiency and accuracy in RAG and long-form tasks

Superior Benchmark Performance

Outperforms models like Mistral Large 2 and Llama 3.3 70B on Arena Hard, CRAG, and FinanceBench

Structured Outputs & Tool Use

Supports function calling, JSON-formatted outputs, and advanced tool integration

Capability Examples

Long Context RAG
Summarize the key business insights from this 200K-token enterprise report on market trends, sales data, and competitor analysis: [insert long report text here].
The report highlights a 15% YoY sales growth driven by e-commerce expansion, with Competitor X losing 8% market share due to supply issues. Top insights: diversify into AI tools for 20% efficiency gains; risks include regulatory changes in EU markets. Citations: Sections 45-120, 180-220.
Multilingual Tool Use
In French, analyze this JSON sales data and call the 'forecast_sales' function with average monthly revenue of 50000 EUR for Q1-Q4: {"data": [{"month":1,"rev":45000},{"month":2,"rev":52000},{"month":3,"rev":48000}]}. Return JSON output.
{"analysis": "Les revenus moyens sont de 48333 EUR/mois. Croissance stable avec pic en février.", "function_call": {"name": "forecast_sales", "args": {"avg_revenue": 50000, "quarters": [1,2,3,4]}}, "forecast": "Prévision Q2-Q4: +12% à 56100 EUR/mois."}

How to use

1
Go to Chat

Navigate to the "AI Chat" page.

2
Select Model

Ensure Jamba Large 1.6 is selected.

3
Type Prompt

Ask a question or paste code.

4
Interact

Refine the answer by replying to the AI.

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Is Jamba Large 1.6 better than Claude 3.5 or Gemini? Test same prompts simultaneously in the Chat Playground.

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