K-EXAONE-236B-A23B vs Qwen2.5-Coder 7B Instruct Comparison
Comparing K-EXAONE-236B-A23B and Qwen2.5-Coder 7B Instruct across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
K-EXAONE-236B-A23B outperforms in 1 benchmarks (MMLU-Pro), while Qwen2.5-Coder 7B Instruct is better at 0 benchmarks.
K-EXAONE-236B-A23B significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
K-EXAONE-236B-A23B has 229.0B more parameters than Qwen2.5-Coder 7B Instruct, making it 3271.4% larger.
Context Window
Maximum input and output token capacity
Only K-EXAONE-236B-A23B specifies input context (32,768 tokens). Only K-EXAONE-236B-A23B specifies output context (32,768 tokens).
License
Usage and distribution terms
K-EXAONE-236B-A23B is licensed under a proprietary license, while Qwen2.5-Coder 7B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
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-Coder 7B Instruct was released on 2024-09-19.
K-EXAONE-236B-A23B is 16 months newer than Qwen2.5-Coder 7B Instruct.
Dec 31, 2025
2 months ago
1.3yr newerSep 19, 2024
1.5 years ago
Knowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while Qwen2.5-Coder 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-Coder 7B Instruct's cutoff date.
Oct 2025
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Outputs Comparison
Key Takeaways
K-EXAONE-236B-A23B
View detailsLG AI Research
Qwen2.5-Coder 7B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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