GLM-5.3-Flash vs o4-mini
GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is 8.1x cheaper per token.
Zhipu AI · OpenAI · Updated for 2026
Which is better?
GLM-5.3-Flash outperforms in 2 benchmarks (CharXiv-R, Humanity's Last Exam), while o4-mini is better at 0 benchmarks. GLM-5.3-Flash significantly outperforms across most benchmarks.
On price, GLM-5.3-Flash is roughly 8.1x 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 want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 8.1x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose o4-mini
- you want predictable pricing at $1.10/M input and $4.40/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5.3-Flash outperforms in 2 benchmarks (CharXiv-R, Humanity's Last Exam), while o4-mini is better at 0 benchmarks.
GLM-5.3-Flash significantly outperforms across most benchmarks.
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 7.3x cheaper than o4-mini ($1.10/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 8.8x cheaper than o4-mini ($4.40/1M tokens).
In conclusion, o4-mini is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to o4-mini's 200,000 tokens. GLM-5.3-Flash can generate longer responses up to 131,072 tokens, while o4-mini is limited to 100,000 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-5.3-Flash and o4-mini support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
o4-mini
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while o4-mini uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while o4-mini was released on 2025-04-16.
GLM-5.3-Flash is 17 months newer than o4-mini.
Aug 26, 2026
1 days ago
1.4yr newerApr 16, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
o4-mini has a documented knowledge cutoff of 2024-05-31, while GLM-5.3-Flash's cutoff date is not specified.
We can confirm o4-mini's training data extends to 2024-05-31, but cannot make a direct comparison without GLM-5.3-Flash's cutoff date.
—
May 2024
Provider Availability
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. o4-mini is available from OpenAI.
GLM-5.3-Flash
o4-mini
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and o4-mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs o4-mini.