Model Comparison

GLM-5 vs K-EXAONE-236B-A23BWhich is better in 2026?

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

Verdict: GLM-5 vs K-EXAONE-236B-A23B — which is better?

GLM-5 (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.

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.

On price, K-EXAONE-236B-A23B is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GLM-5 also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose GLM-5 if…

  • you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
  • you process long inputs — it offers a 200,000 token context window
  • you want the most recent training data — it shipped Feb 2026
  • you need open weights you can self-host or fine-tune

Choose K-EXAONE-236B-A23B if…

  • cost matters — it's about 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.

Mon Jun 08 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
Mon Jun 08 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerFriendliAI
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

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
Mon Jun 08 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

3 months ago

1mo newer
K-EXAONE-236B-A23B

Dec 31, 2025

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

GLM-5

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

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 (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.

Which is better, GLM-5 or 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.

How does GLM-5 compare to K-EXAONE-236B-A23B in benchmarks?

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%.

Is GLM-5 cheaper than K-EXAONE-236B-A23B?

K-EXAONE-236B-A23B is 1.7x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via friendli. K-EXAONE-236B-A23B costs $0.60/M input and $1.00/M output via friendli.

What are the context window sizes for GLM-5 and K-EXAONE-236B-A23B?

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.

What are the main differences between GLM-5 and K-EXAONE-236B-A23B?

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.

Who makes GLM-5 and K-EXAONE-236B-A23B?

GLM-5 is developed by Zhipu AI and K-EXAONE-236B-A23B is developed by LG AI Research.

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