Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 27.5% higher average benchmark score. Jamba 1.5 Mini offers 256.3K more tokens in context window than Phi-4-multimodal-instruct. Both models have similar pricing. Phi-4-multimodal-instruct supports multimodal inputs. Overall, Phi-4-multimodal-instruct is the stronger choice for coding tasks.
AI21 Labs
Jamba 1.5 Mini is a language model developed by AI21 Labs. The model shows competitive results across 8 benchmarks. It excels particularly in ARC-C (85.7%), GSM8k (75.8%), MMLU (69.7%). 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 Mini
AI21 Labs
2024-08-22
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Cost per million tokens (USD)
Jamba 1.5 Mini
Phi-4-multimodal-instruct
Context window and performance specifications
Average performance across 23 common benchmarks
Jamba 1.5 Mini
Phi-4-multimodal-instruct
Jamba 1.5 Mini
2024-03-05
Phi-4-multimodal-instruct
2024-06-01
Available providers and their performance metrics
Jamba 1.5 Mini
Bedrock
Phi-4-multimodal-instruct
Jamba 1.5 Mini
Phi-4-multimodal-instruct
Jamba 1.5 Mini
Phi-4-multimodal-instruct
DeepInfra