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Jamba 1.5 Large vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-MoE-instruct leads with 47.1% higher average benchmark score. Jamba 1.5 Large 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 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.
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Microsoft
Phi-3.5-MoE-instruct is a language model developed by Microsoft. It achieves strong performance with an average score of 65.6% across 31 benchmarks. It excels particularly in ARC-C (91.0%), OpenBookQA (89.6%), GSM8k (88.7%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.
1 days newer
Jamba 1.5 Large
AI21 Labs
2024-08-22
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
Performance Metrics
Context window and performance specifications
Average performance across 32 common benchmarks
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Jamba 1.5 Large
Average Score:16.4%
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Phi-3.5-MoE-instruct
Average Score:63.5%(+47.1%)
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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:16.4%
Providers:2
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Phi-3.5-MoE-instruct
Avg Score:63.5%(+47.1%)
Providers:0