Model Comparison

GLM-4.5V vs K-EXAONE-236B-A23BWhich is better in 2026?

Comparing GLM-4.5V and K-EXAONE-236B-A23B across benchmarks, pricing, and capabilities.

Verdict: GLM-4.5V vs K-EXAONE-236B-A23B — which is better?

GLM-4.5V (by Zhipu AI) and K-EXAONE-236B-A23B (by LG AI Research) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

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.

Choose GLM-4.5V if…

  • 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 if…

  • cost matters — it's about 1.4x cheaper per token
  • you want the most recent training data — it shipped Dec 2025
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

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

Human preference votes

Pricing Analysis

Price comparison per million tokens

K-EXAONE-236B-A23B costs less

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

Lowest available price from all providers
Sat Jun 13 2026 • llm-stats.com
Zhipu AI
GLM-4.5V
Input tokens$0.55
Output tokens$2.19
Best providerFireworks
LG AI Research
K-EXAONE-236B-A23B
Input tokens$0.60
Output tokens$1.00
Best providerFriendliAI
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

128.0B diff

K-EXAONE-236B-A23B has 128.0B more parameters than GLM-4.5V, making it 118.5% larger.

Zhipu AI
GLM-4.5V
108.0Bparameters
LG AI Research
K-EXAONE-236B-A23B
236.0Bparameters
108.0B
GLM-4.5V
236.0B
K-EXAONE-236B-A23B

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.

Zhipu AI
GLM-4.5V
Input131,072 tokens
Output131,072 tokens
LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Sat Jun 13 2026 • llm-stats.com

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

Text
Images
Audio
Video

K-EXAONE-236B-A23B

Text
Images
Audio
Video

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.

GLM-4.5V

MIT

Open weights

K-EXAONE-236B-A23B

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.

GLM-4.5V

Aug 11, 2025

10 months ago

K-EXAONE-236B-A23B

Dec 31, 2025

5 months ago

4mo newer

Knowledge 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.

GLM-4.5V

—

K-EXAONE-236B-A23B

Oct 2025

Provider Availability

GLM-4.5V is available from Fireworks, Novita. K-EXAONE-236B-A23B is available from FriendliAI.

GLM-4.5V

fireworks logo
Fireworks
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

K-EXAONE-236B-A23B

friendli logo
FriendliAI
Input Price:Input: $0.60/1MOutput Price:Output: $1.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (131,072 tokens)
Supports multimodal inputs
Less expensive input tokens
Has open weights
LG AI Research
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5V
LG AI Research
K-EXAONE-236B-A23B

FAQ

Common questions about GLM-4.5V vs K-EXAONE-236B-A23B.

Which is better, GLM-4.5V or K-EXAONE-236B-A23B?

GLM-4.5V (Zhipu AI) and K-EXAONE-236B-A23B (LG AI Research) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does GLM-4.5V compare to K-EXAONE-236B-A23B in benchmarks?

K-EXAONE-236B-A23B scores AIME 2025: 92.8%, MMMLU: 85.7%, MMLU-Pro: 83.8%, LiveCodeBench v6: 80.7%, t2-bench: 73.2%.

Is GLM-4.5V cheaper than K-EXAONE-236B-A23B?

GLM-4.5V is 1.1x cheaper for input tokens. GLM-4.5V costs $0.55/M input and $2.19/M output via fireworks. K-EXAONE-236B-A23B costs $0.60/M input and $1.00/M output via friendli.

What are the context window sizes for GLM-4.5V and K-EXAONE-236B-A23B?

GLM-4.5V supports 131K tokens and K-EXAONE-236B-A23B supports 33K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.5V and K-EXAONE-236B-A23B?

Key differences include context window (131K vs 33K), input pricing ($0.55 vs $0.60/M), multimodal support (yes vs no), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.5V and K-EXAONE-236B-A23B?

GLM-4.5V is developed by Zhipu AI and K-EXAONE-236B-A23B is developed by LG AI Research.

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