Model Comparison

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

Comparing K-EXAONE-236B-A23B and QwQ-32B-Preview 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-Preview 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

QwQ-32B-Preview costs less

For input processing, K-EXAONE-236B-A23B ($0.60/1M tokens) is 4.0x more expensive than QwQ-32B-Preview ($0.15/1M tokens).

For output processing, K-EXAONE-236B-A23B ($1.00/1M tokens) is 5.0x more expensive than QwQ-32B-Preview ($0.20/1M tokens).

In conclusion, K-EXAONE-236B-A23B is more expensive than QwQ-32B-Preview.*

* 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
QwQ-32B-Preview
Input tokens$0.15
Output tokens$0.20
Best providerDeepinfra
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-Preview, making it 626.2% larger.

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

Context Window

Maximum input and output token capacity

Both models have the same input context window of 32,768 tokens. Both models can generate responses up to 32,768 tokens.

LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
Input32,768 tokens
Output32,768 tokens
Thu Apr 16 2026 • llm-stats.com

License

Usage and distribution terms

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

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-Preview was released on 2024-11-28.

K-EXAONE-236B-A23B is 13 months newer than QwQ-32B-Preview.

K-EXAONE-236B-A23B

Dec 31, 2025

3 months ago

1.1yr newer
QwQ-32B-Preview

Nov 28, 2024

1.4 years ago

Knowledge Cutoff

When training data ends

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

K-EXAONE-236B-A23B

Oct 2025

11 mo newer
QwQ-32B-Preview

Nov 2024

Provider Availability

K-EXAONE-236B-A23B is available from FriendliAI. QwQ-32B-Preview is available from DeepInfra, Hyperbolic, Fireworks, Together.

K-EXAONE-236B-A23B

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

QwQ-32B-Preview

deepinfra logo
Deepinfra
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
together logo
Together
Input Price:Input: $1.20/1MOutput Price:Output: $1.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

LG AI Research
Alibaba Cloud / Qwen Team

QwQ-32B-Preview

View details

Alibaba Cloud / Qwen Team

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
QwQ-32B-Preview

FAQ

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

K-EXAONE-236B-A23B (LG AI Research) and QwQ-32B-Preview (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-Preview scores MATH-500: 90.6%, GPQA: 65.2%, AIME 2024: 50.0%, LiveCodeBench: 50.0%.
QwQ-32B-Preview is 4.0x cheaper for input tokens. K-EXAONE-236B-A23B costs $0.60/M input and $1.00/M output via friendli. QwQ-32B-Preview costs $0.15/M input and $0.20/M output via deepinfra.
K-EXAONE-236B-A23B supports 33K tokens and QwQ-32B-Preview supports 33K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include input pricing ($0.60 vs $0.15/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 QwQ-32B-Preview is developed by Alibaba Cloud / Qwen Team.