Model Comparison

K-EXAONE-236B-A23B vs QwQ-32B

Comparing K-EXAONE-236B-A23B and QwQ-32B across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

K-EXAONE-236B-A23B and QwQ-32B don'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

Cost data unavailable.

Lowest available price from all providers
Wed Apr 15 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
QwQ-32B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

203.5B diff

K-EXAONE-236B-A23B has 203.5B more parameters than QwQ-32B, making it 626.2% larger.

LG AI Research
K-EXAONE-236B-A23B
236.0Bparameters
Alibaba Cloud / Qwen Team
QwQ-32B
32.5Bparameters
236.0B
K-EXAONE-236B-A23B
32.5B
QwQ-32B

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).

LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Alibaba Cloud / Qwen Team
QwQ-32B
Input- tokens
Output- tokens
Wed Apr 15 2026 • llm-stats.com

License

Usage and distribution terms

K-EXAONE-236B-A23B is licensed under a proprietary license, while QwQ-32B 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

QwQ-32B

Apache 2.0

Open weights

Release Timeline

When each model was launched

K-EXAONE-236B-A23B was released on 2025-12-31, while QwQ-32B was released on 2025-03-05.

K-EXAONE-236B-A23B is 10 months newer than QwQ-32B.

K-EXAONE-236B-A23B

Dec 31, 2025

3 months ago

10mo newer
QwQ-32B

Mar 5, 2025

1.1 years ago

Knowledge Cutoff

When training data ends

K-EXAONE-236B-A23B has a knowledge cutoff of 2025-10-01, while QwQ-32B has a cutoff of 2024-11-28.

K-EXAONE-236B-A23B has more recent training data (up to 2025-10-01), making it potentially better informed about events through that date compared to QwQ-32B (2024-11-28).

K-EXAONE-236B-A23B

Oct 2025

11 mo newer
QwQ-32B

Nov 2024

Outputs Comparison

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Key Takeaways

LG AI Research
Larger context window (32,768 tokens)
Alibaba Cloud / Qwen Team

QwQ-32B

View details

Alibaba Cloud / Qwen Team

Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
LG AI Research
K-EXAONE-236B-A23B
Alibaba Cloud / Qwen Team
QwQ-32B

FAQ

Common questions about K-EXAONE-236B-A23B vs QwQ-32B

K-EXAONE-236B-A23B (LG AI Research) and QwQ-32B (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
K-EXAONE-236B-A23B scores AIME 2025: 92.8%, MMMLU: 85.7%, MMLU-Pro: 83.8%, LiveCodeBench v6: 80.7%, t2-bench: 73.2%. QwQ-32B scores MATH-500: 90.6%, IFEval: 83.9%, AIME 2024: 79.5%, LiveBench: 73.1%, BFCL: 66.4%.
K-EXAONE-236B-A23B supports 33K tokens and QwQ-32B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include 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 QwQ-32B is developed by Alibaba Cloud / Qwen Team.