GLM-4.7 vs K-EXAONE-236B-A23B
GLM-4.7 significantly outperforms across most benchmarks. K-EXAONE-236B-A23B is 1.4x cheaper per token.
Zhipu AI · LG AI Research · Updated for 2026
Which is better?
GLM-4.7 outperforms in 3 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro), while K-EXAONE-236B-A23B is better at 0 benchmarks. GLM-4.7 significantly outperforms across most benchmarks.
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-4.7 also accepts a larger context window (202,800 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-4.7
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- you process long inputs — it offers a 202,800 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-4.7 outperforms in 3 benchmarks (AIME 2025, LiveCodeBench v6, MMLU-Pro), while K-EXAONE-236B-A23B is better at 0 benchmarks.
GLM-4.7 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.60/1M tokens) costs the same as K-EXAONE-236B-A23B ($0.60/1M tokens).
For output processing, GLM-4.7 ($2.20/1M tokens) is 2.2x more expensive than K-EXAONE-236B-A23B ($1.00/1M tokens).
In conclusion, GLM-4.7 is more expensive than K-EXAONE-236B-A23B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.7 has 122.0B more parameters than K-EXAONE-236B-A23B, making it 51.7% larger.
Context Window
Maximum input and output token capacity
GLM-4.7 accepts 202,800 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. GLM-4.7 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-4.7 supports multimodal inputs, whereas K-EXAONE-236B-A23B does not.
GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-4.7
K-EXAONE-236B-A23B
License
Usage and distribution terms
GLM-4.7 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-4.7 was released on 2025-12-22, while K-EXAONE-236B-A23B was released on 2025-12-31.
K-EXAONE-236B-A23B is 0 month newer than GLM-4.7.
Dec 22, 2025
8 months ago
Dec 31, 2025
7 months ago
1w newerKnowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while GLM-4.7'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-4.7's cutoff date.
—
Oct 2025
Provider Availability
GLM-4.7 is available from Fireworks, Novita. K-EXAONE-236B-A23B is available from FriendliAI.
GLM-4.7
K-EXAONE-236B-A23B
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.7 and K-EXAONE-236B-A23B side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.7 vs K-EXAONE-236B-A23B.