Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 40.5% higher average benchmark score. Jamba 1.5 Large offers 256.0K more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $9.80 cheaper per million tokens. Overall, Phi-3.5-mini-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-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. 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-mini-instruct
Microsoft
2024-08-23
Cost per million tokens (USD)
Jamba 1.5 Large
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 32 common benchmarks
Jamba 1.5 Large
Phi-3.5-mini-instruct
Jamba 1.5 Large
2024-03-05
Available providers and their performance metrics
Jamba 1.5 Large
Bedrock
Phi-3.5-mini-instruct
Jamba 1.5 Large
Phi-3.5-mini-instruct
Jamba 1.5 Large
Phi-3.5-mini-instruct
Azure