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Jamba 1.5 Mini vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-MoE-instruct leads with 7.1% higher average benchmark score. Jamba 1.5 Mini is available on 2 providers. Overall, Phi-3.5-MoE-instruct is the stronger choice for coding tasks.
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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 MoE was created using a mixture-of-experts architecture, designed to provide enhanced capabilities while maintaining efficiency through sparse activation. Built to combine the benefits of larger models with practical computational requirements, it represents Microsoft's exploration of efficient scaling techniques.
1 days newer
Jamba 1.5 Mini
AI21 Labs
2024-08-22
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
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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-MoE-instruct
Average Score:65.2%(+7.1%)
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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-MoE-instruct
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Jamba 1.5 Mini
Avg Score:58.0%
Providers:2
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Phi-3.5-MoE-instruct
Avg Score:65.2%(+7.1%)
Providers:0