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Jamba 1.5 Mini vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 1.8% higher average benchmark score. Jamba 1.5 Mini offers 256.3K more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. Both models have their strengths depending on your specific coding needs.
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AI21 Labs
Jamba 1.5 Mini was created as a more compact hybrid model, designed to bring the benefits of Jamba's architecture to resource-conscious deployments. Built to provide long-context capabilities with reduced computational requirements, it enables efficient processing of extended inputs in practical applications.
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Microsoft
Phi-3.5 Mini was developed by Microsoft as a small language model designed to deliver impressive performance despite its compact size. Built with efficiency in mind, it demonstrates that capable language understanding and generation can be achieved with fewer parameters, making AI more accessible for edge and resource-constrained deployments.
1 days newer
Jamba 1.5 Mini
AI21 Labs
2024-08-22
Phi-3.5-mini-instruct
Microsoft
2024-08-23
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Pricing Comparison
Cost per million tokens (USD)
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Jamba 1.5 Mini
Input:$0.20
Output:$0.40
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($0.40 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 7 common benchmarks
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Jamba 1.5 Mini
Average Score:58.0%
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Phi-3.5-mini-instruct
Average Score:59.8%(+1.8%)
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Knowledge Cutoff
Training data recency comparison
Jamba 1.5 Mini
2024-03-05
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Jamba 1.5 Mini
2 providers
Bedrock
Throughput: 100 tok/s
Latency: 0.5ms
Throughput: 100 tok/s
Latency: 0.3ms
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Phi-3.5-mini-instruct
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Jamba 1.5 Mini
Avg Score:58.0%
Providers:2
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Phi-3.5-mini-instruct
Avg Score:59.8%(+1.8%)
Providers:1