Model Comparison
Comprehensive side-by-side analysis of model capabilities and performance
Jamba 1.5 Large
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.

Phi-4-multimodal-instruct
Microsoft
Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). The model shows particular specialization in general tasks with an average performance of 75.8%. It supports a 256K token context window for handling large documents. The model is available through 1 API provider. 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 2025, it represents Microsoft's latest advancement in AI technology.
Jamba 1.5 Large
AI21 Labs
2024-08-22

Phi-4-multimodal-instruct
Microsoft
2025-02-01
5 months newer
Pricing Comparison
Cost per million tokens (USD)
Jamba 1.5 Large

Phi-4-multimodal-instruct
Performance Metrics
Context window and performance specifications
Jamba 1.5 Large

Phi-4-multimodal-instruct
Performance comparison across key benchmark categories
Jamba 1.5 Large

Phi-4-multimodal-instruct
Jamba 1.5 Large
2024-03-05
Phi-4-multimodal-instruct
2024-06-01
Provider Availability & Performance
Available providers and their performance metrics
Jamba 1.5 Large
Bedrock

Phi-4-multimodal-instruct
DeepInfra
Jamba 1.5 Large

Phi-4-multimodal-instruct