GLM-5.3-Flash vs Phi-4-multimodal-instruct
GLM-5.3-Flash leads the LLM Stats Score 51.6 to 3.4. Phi-4-multimodal-instruct is 3.8x cheaper per token.
Zhipu AI · Microsoft · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 3.4, ranking #11 overall.
On price, Phi-4-multimodal-instruct is roughly 3.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- overall performance matters — it scores 51.6 and ranks #11 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Phi-4-multimodal-instruct
- cost matters — it's about 3.8x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for GLM-5.3-Flash · 15 for Phi-4-multimodal-instruct
GLM-5.3-Flash and Phi-4-multimodal-instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 3.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 5.0x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, GLM-5.3-Flash is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 314.4B more parameters than Phi-4-multimodal-instruct, making it 5614.3% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while Phi-4-multimodal-instruct is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Phi-4-multimodal-instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
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-5.3-Flash was released on 2026-08-26, while Phi-4-multimodal-instruct was released on 2025-02-01.
GLM-5.3-Flash is 19 months newer than Phi-4-multimodal-instruct.
Aug 26, 2026
4 days ago
1.6yr newerFeb 1, 2025
1.6 years ago
Knowledge Cutoff
When training data ends
Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while GLM-5.3-Flash'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-5.3-Flash's cutoff date.
—
Jun 2024
Provider Availability
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Phi-4-multimodal-instruct is available from DeepInfra.
GLM-5.3-Flash
Phi-4-multimodal-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Phi-4-multimodal-instruct.