Model Comparison

K-EXAONE-236B-A23B vs Qwen2.5 7B Instruct

K-EXAONE-236B-A23B significantly outperforms across most benchmarks. Qwen2.5 7B Instruct is 2.3x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

K-EXAONE-236B-A23B outperforms in 1 benchmarks (MMLU-Pro), while Qwen2.5 7B Instruct is better at 0 benchmarks.

K-EXAONE-236B-A23B significantly outperforms across most benchmarks.

Thu Apr 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen2.5 7B Instruct costs less

For input processing, K-EXAONE-236B-A23B ($0.60/1M tokens) is 2.0x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).

For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 3.3x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).

In conclusion, K-EXAONE-236B-A23B is more expensive than Qwen2.5 7B Instruct.*

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

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
LG AI Research
K-EXAONE-236B-A23B
Input tokens$0.60
Output tokens$1.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
Input tokens$0.30
Output tokens$0.30
Best providerTogether
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

228.4B diff

K-EXAONE-236B-A23B has 228.4B more parameters than Qwen2.5 7B Instruct, making it 3001.2% larger.

LG AI Research
K-EXAONE-236B-A23B
236.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
7.6Bparameters
236.0B
K-EXAONE-236B-A23B
7.6B
Qwen2.5 7B Instruct

Context Window

Maximum input and output token capacity

Qwen2.5 7B Instruct accepts 131,072 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. K-EXAONE-236B-A23B can generate longer responses up to 32,768 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.

LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
Input131,072 tokens
Output8,192 tokens
Thu Apr 16 2026 • llm-stats.com

License

Usage and distribution terms

K-EXAONE-236B-A23B is licensed under a proprietary license, while Qwen2.5 7B Instruct uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

K-EXAONE-236B-A23B

Proprietary

Closed source

Qwen2.5 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

K-EXAONE-236B-A23B was released on 2025-12-31, while Qwen2.5 7B Instruct was released on 2024-09-19.

K-EXAONE-236B-A23B is 16 months newer than Qwen2.5 7B Instruct.

K-EXAONE-236B-A23B

Dec 31, 2025

3 months ago

1.3yr newer
Qwen2.5 7B Instruct

Sep 19, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while Qwen2.5 7B Instruct'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 Qwen2.5 7B Instruct's cutoff date.

K-EXAONE-236B-A23B

Oct 2025

Qwen2.5 7B Instruct

—

Provider Availability

K-EXAONE-236B-A23B is available from FriendliAI. Qwen2.5 7B Instruct is available from Together.

K-EXAONE-236B-A23B

friendli logo
Unknown Organization
Input Price:Input: $0.60/1MOutput Price:Output: $1.00/1M

Qwen2.5 7B Instruct

together logo
Together
Input Price:Input: $0.30/1MOutput Price:Output: $0.30/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

LG AI Research
Higher MMLU-Pro score (83.8% vs 56.3%)
Alibaba Cloud / Qwen Team

Qwen2.5 7B Instruct

View details

Alibaba Cloud / Qwen Team

Larger context window (131,072 tokens)
Less expensive input tokens
Less expensive output tokens
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
LG AI Research
K-EXAONE-236B-A23B
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct

FAQ

Common questions about K-EXAONE-236B-A23B vs Qwen2.5 7B Instruct

K-EXAONE-236B-A23B significantly outperforms across most benchmarks. K-EXAONE-236B-A23B is made by LG AI Research and Qwen2.5 7B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
K-EXAONE-236B-A23B scores AIME 2025: 92.8%, MMMLU: 85.7%, MMLU-Pro: 83.8%, LiveCodeBench v6: 80.7%, t2-bench: 73.2%. Qwen2.5 7B Instruct scores GSM8k: 91.6%, MT-Bench: 87.5%, HumanEval: 84.8%, MBPP: 79.2%, MATH: 75.5%.
Qwen2.5 7B Instruct is 2.0x cheaper for input tokens. K-EXAONE-236B-A23B costs $0.60/M input and $1.00/M output via friendli. Qwen2.5 7B Instruct costs $0.30/M input and $0.30/M output via together.
K-EXAONE-236B-A23B supports 33K tokens and Qwen2.5 7B Instruct supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (33K vs 131K), input pricing ($0.60 vs $0.30/M), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
K-EXAONE-236B-A23B is developed by LG AI Research and Qwen2.5 7B Instruct is developed by Alibaba Cloud / Qwen Team.