GLM-4.5V vs K-EXAONE-236B-A23B
Comparing GLM-4.5V and K-EXAONE-236B-A23B across benchmarks, pricing, and capabilities.
Zhipu AI · LG AI Research · Updated for 2026
Which is better?
GLM-4.5V and K-EXAONE-236B-A23B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, K-EXAONE-236B-A23B is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-4.5V also accepts a larger context window (131,072 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-4.5V
- you process long inputs — it offers a 131,072 token context window
- you need open weights you can self-host or fine-tune
Choose K-EXAONE-236B-A23B
- cost matters — it's about 1.4x cheaper per token
- you want the most recent training data — it shipped Dec 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5V and K-EXAONE-236B-A23Bdon'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-4.5V ($0.55/1M tokens) is 1.1x cheaper than K-EXAONE-236B-A23B ($0.60/1M tokens).
For output processing, GLM-4.5V ($2.19/1M tokens) is 2.2x more expensive than K-EXAONE-236B-A23B ($1.00/1M tokens).
In conclusion, GLM-4.5V is more expensive than K-EXAONE-236B-A23B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
K-EXAONE-236B-A23B has 128.0B more parameters than GLM-4.5V, making it 118.5% larger.
Context Window
Maximum input and output token capacity
GLM-4.5V accepts 131,072 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. GLM-4.5V can generate longer responses up to 131,072 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
Input Capabilities
Supported data types and modalities
GLM-4.5V supports multimodal inputs, whereas K-EXAONE-236B-A23B does not.
GLM-4.5V can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.5V
K-EXAONE-236B-A23B
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while K-EXAONE-236B-A23B 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-4.5V was released on 2025-08-11, while K-EXAONE-236B-A23B was released on 2025-12-31.
K-EXAONE-236B-A23B is 5 months newer than GLM-4.5V.
Aug 11, 2025
1.0 years ago
Dec 31, 2025
7 months ago
4mo newerKnowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while GLM-4.5V's cutoff date is not specified.
We can confirm K-EXAONE-236B-A23B's training data extends to 2025-10-01, but cannot make a direct comparison without GLM-4.5V's cutoff date.
—
Oct 2025
Provider Availability
GLM-4.5V is available from Fireworks, Novita. K-EXAONE-236B-A23B is available from FriendliAI.
GLM-4.5V
K-EXAONE-236B-A23B
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5V and K-EXAONE-236B-A23B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5V vs K-EXAONE-236B-A23B.