K-EXAONE-236B-A23B vs MiniMax M2.1: Specs & Benchmark Comparison
| Characteristic | K-EXAONE-236B-A23B | MiniMax M2.1 |
|---|---|---|
| Company | LG AI Research | MiniMax |
| Release Date | December 30, 2025 | December 22, 2025 |
| Parameters | 236B | — |
| Multimodal | No | No |
| Context (input) | 33K | 1.0M |
| Context (output) | 33K | 100K |
| Input Price / 1M | $0.60 | $0.30 |
| Output Price / 1M | $1.00 | $1.20 |
| Average Score | 0.8 | 0.9 |
| Benchmarks | ||
| MMLU-Pro | 0.8 | 0.9 |
Visual Benchmark Comparison
K-EXAONE-236B-A23B
MiniMax M2.1
MMLU-Pro0.8 vs 0.9
0.8
0.9
Verdict
MiniMax M2.1 leads in 2 out of 4 comparison categories.
Overall Performance
Both models show comparable average scores: K-EXAONE-236B-A23B — 0.8, MiniMax M2.1 — 0.9.
API Cost
MiniMax M2.1 is 1.1x cheaper: input $0.30/1M vs $0.60/1M tokens.
Context Window
MiniMax M2.1 supports a larger context: 1M vs 33K tokens.
Recency
Both models were released around the same time: 12/30/2025 and 12/22/2025.
More About These Models
Related Comparisons
The K-EXAONE-236B-A23B and MiniMax M2.1 comparison is updated for 2026. Data includes benchmark results, API pricing, context window size and other specifications. For more detailed information, visit the K-EXAONE-236B-A23B or MiniMax M2.1 page. See also the complete list of AI model comparisons.