Model Comparison

GLM-4.5-Air vs Phi-4-multimodal-instruct

Comparing GLM-4.5-Air and Phi-4-multimodal-instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-4.5-Air and Phi-4-multimodal-instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
Zhipu AI
GLM-4.5-Air
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Microsoft
Phi-4-multimodal-instruct
Input tokens$0.05
Output tokens$0.10
Best providerDeepinfra
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Model Size

Parameter count comparison

100.4B diff

GLM-4.5-Air has 100.4B more parameters than Phi-4-multimodal-instruct, making it 1792.9% larger.

Zhipu AI
GLM-4.5-Air
106.0Bparameters
Microsoft
Phi-4-multimodal-instruct
5.6Bparameters
106.0B
GLM-4.5-Air
5.6B
Phi-4-multimodal-instruct

Context Window

Maximum input and output token capacity

Only Phi-4-multimodal-instruct specifies input context (128,000 tokens). Only Phi-4-multimodal-instruct specifies output context (128,000 tokens).

Zhipu AI
GLM-4.5-Air
Input- tokens
Output- tokens
Microsoft
Phi-4-multimodal-instruct
Input128,000 tokens
Output128,000 tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Phi-4-multimodal-instruct supports multimodal inputs, whereas GLM-4.5-Air does not.

Phi-4-multimodal-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.5-Air

Text
Images
Audio
Video

Phi-4-multimodal-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-4.5-Air

MIT

Open weights

Phi-4-multimodal-instruct

MIT

Open weights

Release Timeline

When each model was launched

GLM-4.5-Air was released on 2025-07-28, while Phi-4-multimodal-instruct was released on 2025-02-01.

GLM-4.5-Air is 6 months newer than Phi-4-multimodal-instruct.

GLM-4.5-Air

Jul 28, 2025

8 months ago

5mo newer
Phi-4-multimodal-instruct

Feb 1, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while GLM-4.5-Air's cutoff date is not specified.

We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without GLM-4.5-Air's cutoff date.

GLM-4.5-Air

—

Phi-4-multimodal-instruct

Jun 2024

Outputs Comparison

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Key Takeaways

Zhipu AI
Microsoft
Larger context window (128,000 tokens)
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5-Air
Microsoft
Phi-4-multimodal-instruct

FAQ

Common questions about GLM-4.5-Air vs Phi-4-multimodal-instruct

GLM-4.5-Air (Zhipu AI) and Phi-4-multimodal-instruct (Microsoft) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
GLM-4.5-Air scores MATH-500: 98.1%, AIME 2024: 89.4%, MMLU-Pro: 81.4%, TAU-bench Retail: 77.9%, BFCL-v3: 76.4%. Phi-4-multimodal-instruct scores ScienceQA Visual: 97.5%, DocVQA: 93.2%, MMBench: 86.7%, POPE: 85.6%, OCRBench: 84.4%.
GLM-4.5-Air supports an unknown number of tokens and Phi-4-multimodal-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.
GLM-4.5-Air is developed by Zhipu AI and Phi-4-multimodal-instruct is developed by Microsoft.