Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 24.2% higher average benchmark score. Jamba 1.5 Large offers 256.0K more tokens in context window than Phi-4-multimodal-instruct. Phi-4-multimodal-instruct is $9.85 cheaper per million tokens. Phi-4-multimodal-instruct supports multimodal inputs. Overall, Phi-4-multimodal-instruct is the stronger choice for coding tasks.
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.
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%). 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.
5 months newer
Jamba 1.5 Large
AI21 Labs
2024-08-22
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Cost per million tokens (USD)
Jamba 1.5 Large
Phi-4-multimodal-instruct
Context window and performance specifications
Average performance across 23 common benchmarks
Jamba 1.5 Large
Phi-4-multimodal-instruct
Jamba 1.5 Large
2024-03-05
Phi-4-multimodal-instruct
2024-06-01
Available providers and their performance metrics
Jamba 1.5 Large
Bedrock
Phi-4-multimodal-instruct
Jamba 1.5 Large
Phi-4-multimodal-instruct
Jamba 1.5 Large
Phi-4-multimodal-instruct
DeepInfra