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Jamba 1.6 Large
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Jamba 1.6 Large

Previous generation large hybrid architecture model from AI21 with 94B active/398B total parameters, supporting 256K long context processing. Uses SSM-Transformer hybrid architecture optimized for enterprise deployment. Features function calling support and long document processing capabilities, particularly suitable for enterprise-grade document analysis, complex conversations, and professional applications requiring long context understanding.
Intelligence(Relatively Weak)
Speed(Relatively Slow)
Input Supported Modalities
Yes
Is Reasoning Model
256,000
Context Window
-
Maximum Output Tokens
-
Knowledge Cutoff
Go Compare

Pricing

- /M tokens
Input
- /M tokens
Output
￥25.2 /M tokens
Blended Price

Quick Simple Comparison

Input

Output

Jamba 1.7 Large
￥0.49
Jamba 1.7 Mini
Jamba 1.5 Mini
￥0.2

Basic Parameters

Jamba 1.6 LargeTechnical Parameters
Parameter Count
Not Announced
Context Length
256.00k tokens
Training Data Cutoff
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
Release Date
2025-03-06
Response Speed
60.10,009 tokens/s

Benchmark Scores

Below is the performance of Jamba 1.6 Large in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
28.81
Large Language Model Intelligence Level
Coding Index
17.83
Indicator of AI model performance on coding tasks
Math Index
31.33
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
56.5
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
38.7
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
17.2
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
18.4
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
70
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
58
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
4.7
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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