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Jamba 1.5 Large vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
Jamba 1.5 Large leads with 8.1% higher average benchmark score. Jamba 1.5 Large offers 256.0K more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $9.80 cheaper per million tokens. Overall, Jamba 1.5 Large is the stronger choice for coding tasks.
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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 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 Large
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 Large
Input:$2.00
Output:$8.00
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($9.80 cheaper)
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%(+8.1%)
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Phi-3.5-mini-instruct
Average Score:59.8%
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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-mini-instruct
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Jamba 1.5 Large
Avg Score:67.9%(+8.1%)
Providers:2
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Phi-3.5-mini-instruct
Avg Score:59.8%
Providers:1