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GLM-4.6 vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
GLM-4.6 leads with 50.6% 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, GLM-4.6 is the stronger choice for coding tasks.
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Zhipu AI
GLM-4.6 was introduced as an enhanced iteration of the GLM-4 series, designed to provide improved capabilities in bilingual language understanding and generation. Built to incorporate refinements to the GLM architecture, it represents continued advancement in Zhipu AI's model development.
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Microsoft
Phi-3.5 Mini was developed by Microsoft as a small language model designed to deliver impressive performance despite its compact size. Built with efficiency in mind, it demonstrates that capable language understanding and generation can be achieved with fewer parameters, making AI more accessible for edge and resource-constrained deployments.
1 year newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
GLM-4.6
Zhipu AI
2025-09-30
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Pricing Comparison
Cost per million tokens (USD)
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GLM-4.6
Input:$0.60
Output:$2.00
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Phi-3.5-mini-instruct
Input:$0.10
Output:$0.10($2.40 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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GLM-4.6
Average Score:81.0%(+50.6%)
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Phi-3.5-mini-instruct
Average Score:30.4%
Provider Availability & Performance
Available providers and their performance metrics
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GLM-4.6
2 providers
DeepInfra
Throughput: 85 tok/s
Latency: 0.7ms
ZeroEval
Throughput: 85 tok/s
Latency: 0.7ms
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Phi-3.5-mini-instruct
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GLM-4.6
Avg Score:81.0%(+50.6%)
Providers:2
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Phi-3.5-mini-instruct
Avg Score:30.4%
Providers:1