Model Comparison
GLM-5 vs Phi-4-multimodal-instructWhich is better in 2026?
Comparing GLM-5 and Phi-4-multimodal-instruct across benchmarks, pricing, and capabilities.
Verdict: GLM-5 vs Phi-4-multimodal-instruct — which is better?
GLM-5 (by Zhipu AI) and Phi-4-multimodal-instruct (by Microsoft) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, Phi-4-multimodal-instruct is roughly 24.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5 also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-5 if…
- you process long inputs — it offers a 200,000 token context window
- you want the most recent training data — it shipped Feb 2026
Choose Phi-4-multimodal-instruct if…
- cost matters — it's about 24.8x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 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
For input processing, GLM-5 ($1.00/1M tokens) is 20.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 32.0x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, GLM-5 is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 738.4B more parameters than Phi-4-multimodal-instruct, making it 13185.7% larger.
Context Window
Maximum input and output token capacity
GLM-5 accepts 200,000 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Both models can generate responses up to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Phi-4-multimodal-instruct supports multimodal inputs, whereas GLM-5 does not.
Phi-4-multimodal-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
Phi-4-multimodal-instruct
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while Phi-4-multimodal-instruct was released on 2025-02-01.
GLM-5 is 13 months newer than Phi-4-multimodal-instruct.
Feb 11, 2026
3 months ago
1.0yr newerFeb 1, 2025
1.3 years ago
Knowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while GLM-5'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-5's cutoff date.
—
Jun 2024
Provider Availability
GLM-5 is available from FriendliAI, ZAI. Phi-4-multimodal-instruct is available from DeepInfra.
GLM-5
Phi-4-multimodal-instruct
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
Detailed Comparison
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FAQ
Common questions about GLM-5 vs Phi-4-multimodal-instruct.