GLM-4.7 vs K-EXAONE-236B-A23B
GLM-4.7 leads the LLM Stats Score 34.2 to 26.2. K-EXAONE-236B-A23B is 1.1x cheaper per token.
Zhipu AI · LG AI Research · Updated for 2026
Which is better?
GLM-4.7 leads the overall LLM Stats Score 34.2 to 26.2, ranking #105 overall.
In the 3 individual benchmarks reported for both models, GLM-4.7 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, K-EXAONE-236B-A23B is roughly 1.1x 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 (202,752 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-4.7
- overall performance matters — it scores 34.2 and ranks #105 on LLM Stats
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 202,752 token context window
- you need open weights you can self-host or fine-tune
Choose K-EXAONE-236B-A23B
- cost matters — it's about 1.1x cheaper per token
- you want the most recent training data — it shipped Dec 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
13 reported for GLM-4.7 · 6 for K-EXAONE-236B-A23B
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.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.7 ($0.40/1M tokens) is 1.5x cheaper than K-EXAONE-236B-A23B ($0.60/1M tokens).
For output processing, GLM-4.7 ($1.75/1M tokens) is 1.8x 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 202,752 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. GLM-4.7 can generate longer responses up to 202,752 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
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
9 months ago
Dec 31, 2025
9 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 DeepInfra, 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.