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Jamba 1.5 Large vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
Jamba 1.5 Large leads with 2.7% higher average benchmark score. Jamba 1.5 Large is available on 2 providers. Both models have their strengths depending on your specific coding needs.
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AI21 Labs
Jamba 1.5 Large was developed by AI21 Labs using a hybrid architecture combining transformer and state space models, designed to provide efficient long-context understanding. Built to handle extended documents and conversations with computational efficiency, it represents AI21's innovation in efficient large-scale model design.
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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 Large
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 Large
Average Score:67.9%(+2.7%)
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Phi-3.5-MoE-instruct
Average Score:65.2%
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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
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Jamba 1.5 Large
2 providers
Bedrock
Throughput: 100 tok/s
Latency: 0.5ms
Throughput: 42 tok/s
Latency: 0.3ms
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Phi-3.5-MoE-instruct
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Jamba 1.5 Large
Avg Score:67.9%(+2.7%)
Providers:2
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Phi-3.5-MoE-instruct
Avg Score:65.2%
Providers:0