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.

Microsoft

Phi-3.5-mini-instruct

Microsoft

Phi-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.

Jamba 1.5 Large

AI21 Labs

2024-08-22

Microsoft

Phi-3.5-mini-instruct

Microsoft

2024-08-23

1 days newer

Pricing Comparison

Cost per million tokens (USD)

Jamba 1.5 Large

Input:$2.00
Output:$8.00
Microsoft

Phi-3.5-mini-instruct

$9.80 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

Jamba 1.5 Large

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

Phi-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 7 common benchmarks

Jamba 1.5 Large

+8.1%
Average Score:67.9%
Microsoft

Phi-3.5-mini-instruct

Average Score:59.8%

Performance comparison across key benchmark categories

Jamba 1.5 Large

reasoning
+18.8%
93.0%
math
+26.1%
87.0%
factuality
58.3%
general
+1.7%
57.1%
Microsoft

Phi-3.5-mini-instruct

reasoning
74.2%
math
60.9%
factuality
+5.7%
64.0%
general
55.4%
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
Microsoft

Phi-3.5-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms

Jamba 1.5 Large

+8.1%
Avg Score:67.9%
Providers:2
Microsoft

Phi-3.5-mini-instruct

Avg Score:59.8%
Providers:1