Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 35.5% higher average benchmark score. Phi-3.5-mini-instruct offers 120.0K more tokens in context window than Grok-2. Phi-3.5-mini-instruct is $11.80 cheaper per million tokens. Grok-2 supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
xAI
Grok-2 is a multimodal language model developed by xAI. It achieves strong performance with an average score of 76.5% across 8 benchmarks. It excels particularly in DocVQA (93.6%), HumanEval (88.4%), MMLU (87.5%). It supports a 136K 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. Released in 2024, it represents xAI'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.
10 days newer
Grok-2
xAI
2024-08-13
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Cost per million tokens (USD)
Grok-2
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 34 common benchmarks
Grok-2
Phi-3.5-mini-instruct
Available providers and their performance metrics
Grok-2
xAI
Phi-3.5-mini-instruct
Grok-2
Phi-3.5-mini-instruct
Grok-2
Phi-3.5-mini-instruct
Azure