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Jamba 1.5 Large vs Phi-3.5-vision-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-vision-instruct leads with 5.3% higher average benchmark score. Phi-3.5-vision-instruct supports multimodal inputs. Jamba 1.5 Large is available on 2 providers. Overall, Phi-3.5-vision-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-vision-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 68.3% across 9 benchmarks. It excels particularly in ScienceQA (91.3%), POPE (86.1%), MMBench (81.9%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. 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-vision-instruct
Microsoft
2024-08-23
Performance Metrics
Context window and performance specifications
Average performance across 17 common benchmarks
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Jamba 1.5 Large
Average Score:30.8%
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Phi-3.5-vision-instruct
Average Score:36.1%(+5.3%)
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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-vision-instruct
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Jamba 1.5 Large
Avg Score:30.8%
Providers:2
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Phi-3.5-vision-instruct
Avg Score:36.1%(+5.3%)
Providers:0