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-Coder 32B Instruct

Alibaba

Qwen2.5-Coder 32B Instruct is a language model developed by Alibaba. It achieves strong performance with an average score of 64.9% across 15 benchmarks. It excels particularly in HumanEval (92.7%), GSM8k (91.1%), MBPP (90.2%). The model shows particular specialization in reasoning tasks with an average performance of 78.1%. It supports a 256K 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-Coder 32B 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-Coder 32B Instruct

$9.82 cheaper
Input:$0.09
Output:$0.09

Performance Metrics

Context window and performance specifications

Jamba 1.5 Large

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

Qwen2.5-Coder 32B Instruct

Max Context:256.0K
Parameters:32.0B

Average performance across 5 common benchmarks

Jamba 1.5 Large

+6.3%
Average Score:74.6%
Alibaba

Qwen2.5-Coder 32B Instruct

Average Score:68.3%

Performance comparison across key benchmark categories

Jamba 1.5 Large

reasoning
+14.9%
93.0%
math
+12.8%
87.0%
general
57.1%
factuality
+4.1%
58.3%
Alibaba

Qwen2.5-Coder 32B Instruct

reasoning
78.1%
math
74.2%
general
+4.4%
61.5%
factuality
54.2%
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-Coder 32B Instruct

4 providers

Hyperbolic

Throughput: 100 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 44 tok/s
Latency: 0.5ms

Fireworks

Throughput: 110 tok/s
Latency: 0.26ms

Lambda

Throughput: 42 tok/s
Latency: 0.5ms

Jamba 1.5 Large

+6.3%
Avg Score:74.6%
Providers:2
Alibaba

Qwen2.5-Coder 32B Instruct

Avg Score:68.3%
Providers:4