Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 37.6% higher average benchmark score. Phi-3.5-mini-instruct offers 59.4K more tokens in context window than GLM-4.6. Phi-3.5-mini-instruct is $2.40 cheaper per million tokens. GLM-4.6 supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
Zhipu AI
GLM-4.6 is a multimodal language model developed by Zhipu AI. It achieves strong performance with an average score of 61.2% across 7 benchmarks. It excels particularly in AIME 2025 (93.9%), LiveCodeBench v6 (82.8%), GPQA (81.0%). It supports a 197K 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. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Zhipu AI'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 year newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
GLM-4.6
Zhipu AI
2025-09-30
Cost per million tokens (USD)
GLM-4.6
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 37 common benchmarks
GLM-4.6
Phi-3.5-mini-instruct
Available providers and their performance metrics
GLM-4.6
DeepInfra
ZeroEval
Phi-3.5-mini-instruct
GLM-4.6
Phi-3.5-mini-instruct
GLM-4.6
Phi-3.5-mini-instruct
Azure