Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Jamba 1.5 Large

AI21 Labs

Jamba 1.5 Large is a language model developed by AI21 Labs. It achieves strong performance with an average score of 65.5% across 8 benchmarks. It excels particularly in ARC-C (93.0%), GSM8k (87.0%), MMLU (81.2%). It supports a 512K token context window for handling large documents. The model is available through 2 API providers. Released in 2024, it represents AI21 Labs's latest advancement in AI technology.

Alibaba

QvQ-72B-Preview

Alibaba

QvQ-72B-Preview is a multimodal language model developed by Alibaba. The model shows competitive results across 4 benchmarks. Notable strengths include MathVista (71.4%), MMMU (70.3%), MathVision (35.9%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Alibaba's latest advancement in AI technology.

Jamba 1.5 Large

AI21 Labs

2024-08-22

Alibaba

QvQ-72B-Preview

Alibaba

2024-12-25

4 months newer

Performance Metrics

Context window and performance specifications

Jamba 1.5 Large

Larger context
Max Context:512.0K
Parameters:398.0B
Alibaba

QvQ-72B-Preview

Max Context:-
Parameters:73.4B

Performance comparison across key benchmark categories

Jamba 1.5 Large

math
+33.4%
87.0%
general
+36.7%
57.1%
Alibaba

QvQ-72B-Preview

math
53.6%
general
20.4%
Knowledge Cutoff
Training data recency comparison

Jamba 1.5 Large

2024-03-05

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Jamba 1.5 Large

2 providers

Google

Throughput: 42 tok/s
Latency: 0.3ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms
Alibaba

QvQ-72B-Preview

0 providers

Jamba 1.5 Large

Avg Score:0.0%
Providers:2
Alibaba

QvQ-72B-Preview

Avg Score:0.0%
Providers:0