Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 37.3% higher average benchmark score. Grok-4 offers 8.0K more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $17.80 cheaper per million tokens. Grok-4 supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
xAI
Grok-4 is a multimodal language model developed by xAI. It achieves strong performance with an average score of 63.1% across 7 benchmarks. It excels particularly in AIME 2025 (91.7%), HMMT25 (90.0%), GPQA (87.5%). It supports a 264K token context window for handling large documents. The model is available through 2 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, 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 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Grok-4
xAI
2025-07-09
Cost per million tokens (USD)
Grok-4
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 37 common benchmarks
Grok-4
Phi-3.5-mini-instruct
Grok-4
2024-12-31
Available providers and their performance metrics
Grok-4
xAI
ZeroEval
Grok-4
Phi-3.5-mini-instruct
Grok-4
Phi-3.5-mini-instruct
Phi-3.5-mini-instruct
Azure