DeepSeek-V3.1 vs K-EXAONE-236B-A23B: Specs & Benchmark Comparison
| Characteristic | DeepSeek-V3.1 | K-EXAONE-236B-A23B |
|---|---|---|
| Company | DeepSeek | LG AI Research |
| Release Date | January 9, 2025 | December 30, 2025 |
| Parameters | 671B | 236B |
| Multimodal | No | No |
| Context (input) | 164K | 33K |
| Context (output) | 164K | 33K |
| Input Price / 1M | $0.27 | $0.60 |
| Output Price / 1M | $1.00 | $1.00 |
| Average Score | 0.8 | 0.8 |
| Benchmarks | ||
| MMLU-Pro | 0.8 | 0.8 |
Visual Benchmark Comparison
DeepSeek-V3.1
K-EXAONE-236B-A23B
MMLU-Pro0.8 vs 0.8
0.8
0.8
Verdict
DeepSeek-V3.1 leads in 2 out of 4 comparison categories.
Overall Performance
Both models show comparable average scores: DeepSeek-V3.1 — 0.8, K-EXAONE-236B-A23B — 0.8.
API Cost
DeepSeek-V3.1 is 1.3x cheaper: input $0.27/1M vs $0.60/1M tokens.
Context Window
DeepSeek-V3.1 supports a larger context: 164K vs 33K tokens.
Recency
K-EXAONE-236B-A23B is newer: released 12/30/2025 vs 1/9/2025.
More About These Models
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Frequently Asked Questions
Which is better for coding — DeepSeek-V3.1 or K-EXAONE-236B-A23B?
Direct comparison on the SWE-Bench benchmark is not available. We recommend reviewing other metrics on the comparison page.
Which model is cheaper — DeepSeek-V3.1 or K-EXAONE-236B-A23B?
DeepSeek-V3.1 is cheaper for input: $0.27 per 1M tokens vs $0.60.
Which has a larger context window — DeepSeek-V3.1 or K-EXAONE-236B-A23B?
DeepSeek-V3.1 supports a larger context: 163,840 tokens vs 32,768.
The DeepSeek-V3.1 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-V3.1 or K-EXAONE-236B-A23B page. See also the complete list of AI model comparisons.