DeepSeek-V3.2 (Thinking) vs K-EXAONE-236B-A23B: Specs & Benchmark Comparison
| Characteristic | DeepSeek-V3.2 (Thinking) | K-EXAONE-236B-A23B |
|---|---|---|
| Company | DeepSeek | LG AI Research |
| Release Date | November 30, 2025 | December 30, 2025 |
| Parameters | 685B | 236B |
| Multimodal | No | No |
| Context (input) | 131K | 33K |
| Context (output) | 66K | 33K |
| Input Price / 1M | $0.28 | $0.60 |
| Output Price / 1M | $0.42 | $1.00 |
| Average Score | 0.9 | 0.8 |
| Benchmarks | ||
| T2-Bench | 0.8 | 0.7 |
| MMLU-Pro | 0.8 | 0.8 |
| AIME 2025 | 0.9 | 0.9 |
Visual Benchmark Comparison
DeepSeek-V3.2 (Thinking)
K-EXAONE-236B-A23B
T2-Bench0.8 vs 0.7
0.8
0.7
MMLU-Pro0.8 vs 0.8
0.8
0.8
AIME 20250.9 vs 0.9
0.9
0.9
Verdict
DeepSeek-V3.2 (Thinking) leads in 2 out of 4 comparison categories.
Overall Performance
Both models show comparable average scores: DeepSeek-V3.2 (Thinking) — 0.9, K-EXAONE-236B-A23B — 0.8.
API Cost
DeepSeek-V3.2 (Thinking) is 2.3x cheaper: input $0.28/1M vs $0.60/1M tokens.
Context Window
DeepSeek-V3.2 (Thinking) supports a larger context: 131K vs 33K tokens.
Recency
K-EXAONE-236B-A23B is newer: released 12/30/2025 vs 11/30/2025.
More About These Models
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The DeepSeek-V3.2 (Thinking) and K-EXAONE-236B-A23B comparison is updated for 2026. Data includes benchmark results, API pricing, context window size and other specifications. For more detailed information, visit the DeepSeek-V3.2 (Thinking) or K-EXAONE-236B-A23B page. See also the complete list of AI model comparisons.