Model Comparison
DeepSeek-V3.2 (Thinking) vs K-EXAONE-236B-A23BWhich is better in 2026?
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is 2.2x cheaper per token.
Verdict: DeepSeek-V3.2 (Thinking) vs K-EXAONE-236B-A23B — which is better?
DeepSeek-V3.2 (Thinking) (by DeepSeek) and K-EXAONE-236B-A23B (by LG AI Research) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, MMLU-Pro, t2-bench), while K-EXAONE-236B-A23B is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2 (Thinking) is roughly 2.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.2 (Thinking) also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2 (Thinking) if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- cost matters — it's about 2.2x cheaper per token
- you process long inputs — it offers a 131,072 token context window
- you need open weights you can self-host or fine-tune
Choose K-EXAONE-236B-A23B if…
- you want the most recent training data — it shipped Dec 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, MMLU-Pro, t2-bench), while K-EXAONE-236B-A23B is better at 0 benchmarks.
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 2.1x cheaper than K-EXAONE-236B-A23B ($0.60/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 2.4x cheaper than K-EXAONE-236B-A23B ($1.00/1M tokens).
In conclusion, K-EXAONE-236B-A23B is more expensive than DeepSeek-V3.2 (Thinking).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Thinking) has 449.0B more parameters than K-EXAONE-236B-A23B, making it 190.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.2 (Thinking) accepts 131,072 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. DeepSeek-V3.2 (Thinking) can generate longer responses up to 65,536 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) is licensed under MIT, 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.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while K-EXAONE-236B-A23B was released on 2025-12-31.
K-EXAONE-236B-A23B is 1 month newer than DeepSeek-V3.2 (Thinking).
Dec 1, 2025
7 months ago
Dec 31, 2025
6 months ago
1mo newerKnowledge Cutoff
When training data ends
K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while DeepSeek-V3.2 (Thinking)'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 DeepSeek-V3.2 (Thinking)'s cutoff date.
—
Oct 2025
Provider Availability
DeepSeek-V3.2 (Thinking) is available from DeepSeek. K-EXAONE-236B-A23B is available from FriendliAI.
DeepSeek-V3.2 (Thinking)
K-EXAONE-236B-A23B
Outputs Comparison
Key Takeaways
K-EXAONE-236B-A23B
View detailsLG AI Research
No standout differentiators in the data we have for this pair.
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) and K-EXAONE-236B-A23B side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs K-EXAONE-236B-A23B.