Qwen3.5-397B-A17B vs K-EXAONE-236B-A23B Comparison

Comparing Qwen3.5-397B-A17B and K-EXAONE-236B-A23B across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Qwen3.5-397B-A17B outperforms in 5 benchmarks (IFBench, LiveCodeBench v6, MMLU-Pro, MMMLU, t2-bench), while K-EXAONE-236B-A23B is better at 0 benchmarks.

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

Tue Mar 17 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, Qwen3.5-397B-A17B ($0.60/1M tokens) costs the same as K-EXAONE-236B-A23B ($0.60/1M tokens).

For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 3.6x more expensive than K-EXAONE-236B-A23B ($1.00/1M tokens).

In conclusion, Qwen3.5-397B-A17B is more expensive than K-EXAONE-236B-A23B.*

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

Lowest available price from all providers
Tue Mar 17 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input tokens$0.60
Output tokens$3.60
Best providerNovita
LG AI Research
K-EXAONE-236B-A23B
Input tokens$0.60
Output tokens$1.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

161.0B diff

Qwen3.5-397B-A17B has 161.0B more parameters than K-EXAONE-236B-A23B, making it 68.2% larger.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
397.0Bparameters
LG AI Research
K-EXAONE-236B-A23B
236.0Bparameters
397.0B
Qwen3.5-397B-A17B
236.0B
K-EXAONE-236B-A23B

Context Window

Maximum input and output token capacity

Qwen3.5-397B-A17B accepts 262,144 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. Qwen3.5-397B-A17B can generate longer responses up to 64,000 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input262,144 tokens
Output64,000 tokens
LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Tue Mar 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.5-397B-A17B supports multimodal inputs, whereas K-EXAONE-236B-A23B does not.

Qwen3.5-397B-A17B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen3.5-397B-A17B

Text
Images
Audio
Video

K-EXAONE-236B-A23B

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen3.5-397B-A17B is licensed under Apache 2.0, 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.

Qwen3.5-397B-A17B

Apache 2.0

Open weights

K-EXAONE-236B-A23B

Proprietary

Closed source

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while K-EXAONE-236B-A23B was released on 2025-12-31.

Qwen3.5-397B-A17B is 2 months newer than K-EXAONE-236B-A23B.

Qwen3.5-397B-A17B

Feb 16, 2026

4 weeks ago

1mo newer
K-EXAONE-236B-A23B

Dec 31, 2025

2 months ago

Knowledge Cutoff

When training data ends

K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while Qwen3.5-397B-A17B'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 Qwen3.5-397B-A17B's cutoff date.

Qwen3.5-397B-A17B

K-EXAONE-236B-A23B

Oct 2025

Provider Availability

Qwen3.5-397B-A17B is available from Novita. K-EXAONE-236B-A23B is available from FriendliAI. The availability of providers can affect quality of the model and reliability.

Qwen3.5-397B-A17B

novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

K-EXAONE-236B-A23B

friendli logo
Unknown Organization
Input Price:Input: $0.60/1MOutput Price:Output: $1.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Alibaba Cloud / Qwen Team

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Supports multimodal inputs
Has open weights
Higher IFBench score (76.5% vs 67.3%)
Higher LiveCodeBench v6 score (83.6% vs 80.7%)
Higher MMLU-Pro score (87.8% vs 83.8%)
Higher MMMLU score (88.5% vs 85.7%)
Higher t2-bench score (86.7% vs 73.2%)
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
LG AI Research
K-EXAONE-236B-A23B