Model Comparison

GLM-5 vs K-EXAONE-236B-A23B

GLM-5 significantly outperforms across most benchmarks. K-EXAONE-236B-A23B is 2.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-5 outperforms in 1 benchmarks (t2-bench), while K-EXAONE-236B-A23B is better at 0 benchmarks.

GLM-5 significantly outperforms across most benchmarks.

Tue Apr 07 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

K-EXAONE-236B-A23B costs less

For input processing, GLM-5 ($1.00/1M tokens) is 1.7x more expensive than K-EXAONE-236B-A23B ($0.60/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 3.2x more expensive than K-EXAONE-236B-A23B ($1.00/1M tokens).

In conclusion, GLM-5 is more expensive than K-EXAONE-236B-A23B.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Apr 07 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
LG AI Research
K-EXAONE-236B-A23B
Input tokens$0.60
Output tokens$1.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

508.0B diff

GLM-5 has 508.0B more parameters than K-EXAONE-236B-A23B, making it 215.3% larger.

Zhipu AI
GLM-5
744.0Bparameters
LG AI Research
K-EXAONE-236B-A23B
236.0Bparameters
744.0B
GLM-5
236.0B
K-EXAONE-236B-A23B

Context Window

Maximum input and output token capacity

GLM-5 accepts 200,000 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. GLM-5 can generate longer responses up to 128,000 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Tue Apr 07 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 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-5

MIT

Open weights

K-EXAONE-236B-A23B

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while K-EXAONE-236B-A23B was released on 2025-12-31.

GLM-5 is 1 month newer than K-EXAONE-236B-A23B.

GLM-5

Feb 11, 2026

1 months ago

1mo newer
K-EXAONE-236B-A23B

Dec 31, 2025

3 months ago

Knowledge Cutoff

When training data ends

K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while GLM-5'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-5's cutoff date.

GLM-5

—

K-EXAONE-236B-A23B

Oct 2025

Provider Availability

GLM-5 is available from ZAI. K-EXAONE-236B-A23B is available from FriendliAI.

GLM-5

z logo
Unknown Organization
Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

K-EXAONE-236B-A23B

friendli logo
Unknown Organization
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 (200,000 tokens)
Has open weights
Higher t2-bench score (89.7% vs 73.2%)
LG AI Research
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
LG AI Research
K-EXAONE-236B-A23B

FAQ

Common questions about GLM-5 vs K-EXAONE-236B-A23B

GLM-5 significantly outperforms across most benchmarks. GLM-5 is made by Zhipu AI and K-EXAONE-236B-A23B is made by LG AI Research. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. K-EXAONE-236B-A23B scores AIME 2025: 92.8%, MMMLU: 85.7%, MMLU-Pro: 83.8%, LiveCodeBench v6: 80.7%, t2-bench: 73.2%.
K-EXAONE-236B-A23B is 1.7x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via z. K-EXAONE-236B-A23B costs $0.60/M input and $1.00/M output via friendli.
GLM-5 supports 200K 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.
Key differences include context window (200K vs 33K), input pricing ($1.00 vs $0.60/M), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
GLM-5 is developed by Zhipu AI and K-EXAONE-236B-A23B is developed by LG AI Research.