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GLM-4.5 vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 6.4% higher average benchmark score. GLM-4.5 offers 6.1K more tokens in context window than Phi-4-multimodal-instruct. Phi-4-multimodal-instruct is $1.85 cheaper per million tokens. Phi-4-multimodal-instruct supports multimodal inputs. GLM-4.5 is available on 3 providers. Overall, Phi-4-multimodal-instruct is the stronger choice for coding tasks.
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Zhipu AI
GLM-4.5 is a language model developed by Zhipu AI. It achieves strong performance with an average score of 64.0% across 14 benchmarks. It excels particularly in MATH-500 (98.2%), AIME 2024 (91.0%), MMLU-Pro (84.6%). It supports a 262K token context window for handling large documents. The model is available through 3 API providers. 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.
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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.
5 months newer
Phi-4-multimodal-instruct
Microsoft
2025-02-01
GLM-4.5
Zhipu AI
2025-07-28
Pricing Comparison
Cost per million tokens (USD)
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GLM-4.5
Input:$0.40
Output:$1.60
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Phi-4-multimodal-instruct
Input:$0.05
Output:$0.10($1.85 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 29 common benchmarks
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GLM-4.5
Average Score:30.9%
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Phi-4-multimodal-instruct
Average Score:37.3%(+6.4%)
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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
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GLM-4.5
3 providers
DeepInfra
Novita
ZeroEval
Throughput: 85 tok/s
Latency: 0.7ms
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Phi-4-multimodal-instruct
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GLM-4.5
Avg Score:30.9%
Providers:3
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Phi-4-multimodal-instruct
Avg Score:37.3%(+6.4%)
Providers:1