+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
GLM-4.6 vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 29.6% higher average benchmark score. Phi-4-multimodal-instruct offers 59.4K more tokens in context window than GLM-4.6. Phi-4-multimodal-instruct is $2.45 cheaper per million tokens. Overall, Phi-4-multimodal-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-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.
8 months newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
GLM-4.6
Zhipu AI
2025-09-30
Pricing Comparison
Cost per million tokens (USD)
+
+
+
+
GLM-4.6
Input:$0.60
Output:$2.00
+
+
+
+
Phi-4-multimodal-instruct
Input:$0.05
Output:$0.10($2.45 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 22 common benchmarks
+
+
+
+
GLM-4.6
Average Score:19.5%
+
+
+
+
Phi-4-multimodal-instruct
Average Score:49.1%(+29.6%)
+
+
+
+
Knowledge Cutoff
Training data recency comparison
Phi-4-multimodal-instruct
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
GLM-4.6
2 providers
DeepInfra
Throughput: 85 tok/s
Latency: 0.7ms
ZeroEval
Throughput: 85 tok/s
Latency: 0.7ms
+
+
+
+
Phi-4-multimodal-instruct
+
+
+
+
GLM-4.6
Avg Score:19.5%
Providers:2
+
+
+
+
Phi-4-multimodal-instruct
Avg Score:49.1%(+29.6%)
Providers:1