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

Qwen2.5 72B Instruct

Alibaba

Qwen2.5 72B Instruct is a language model developed by Alibaba. It achieves strong performance with an average score of 77.4% across 14 benchmarks. It excels particularly in GSM8k (95.8%), MT-Bench (93.5%), MBPP (88.2%). The model shows particular specialization in math tasks with an average performance of 89.5%. It supports a 139K token context window for handling large documents. The model is available through 4 API providers. 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

Qwen2.5 72B Instruct

Alibaba

2024-09-19

28 days newer

Pricing Comparison

Cost per million tokens (USD)

Jamba 1.5 Large

Input:$2.00
Output:$8.00
Alibaba

Qwen2.5 72B Instruct

$9.25 cheaper
Input:$0.35
Output:$0.40

Performance Metrics

Context window and performance specifications

Jamba 1.5 Large

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

Qwen2.5 72B Instruct

Max Context:139.3K
Parameters:72.7B

Average performance across 4 common benchmarks

Jamba 1.5 Large

Average Score:60.7%
Alibaba

Qwen2.5 72B Instruct

+13.6%
Average Score:74.3%

Performance comparison across key benchmark categories

Jamba 1.5 Large

math
87.0%
general
57.1%
Alibaba

Qwen2.5 72B Instruct

math
+2.5%
89.5%
general
+17.0%
74.1%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
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

Qwen2.5 72B Instruct

4 providers

Together

Throughput: 47 tok/s
Latency: 0.5ms

Hyperbolic

Throughput: 100 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 10 tok/s
Latency: 0.5ms

Fireworks

Throughput: 59 tok/s
Latency: 0.37ms

Jamba 1.5 Large

Avg Score:60.7%
Providers:2
Alibaba

Qwen2.5 72B Instruct

+13.6%
Avg Score:74.3%
Providers:4