GLM-5.3-Flash vs Phi-3.5-mini-instruct
Comparing GLM-5.3-Flash and Phi-3.5-mini-instruct across benchmarks, pricing, and capabilities.
Zhipu AI · Microsoft · Updated for 2026
Which is better?
GLM-5.3-Flash and Phi-3.5-mini-instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Phi-3.5-mini-instruct is roughly 2.4x 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 benchmark, pricing, and model metadata for 2026.
Choose GLM-5.3-Flash
- 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-3.5-mini-instruct
- cost matters — it's about 2.4x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash and Phi-3.5-mini-instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 1.5x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 5.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).
In conclusion, GLM-5.3-Flash is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5.3-Flash has 316.2B more parameters than Phi-3.5-mini-instruct, making it 8321.1% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while Phi-3.5-mini-instruct is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
GLM-5.3-Flash supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.3-Flash
Phi-3.5-mini-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-3.5-mini-instruct was released on 2024-08-23.
GLM-5.3-Flash is 24 months newer than Phi-3.5-mini-instruct.
Aug 26, 2026
0 days ago
2.0yr newerAug 23, 2024
2.0 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Phi-3.5-mini-instruct is available from Azure.
GLM-5.3-Flash
Phi-3.5-mini-instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Phi-3.5-mini-instruct.