K-EXAONE-236B-A23B vs Kimi K2 0905: Specs & Benchmark Comparison
| Characteristic | K-EXAONE-236B-A23B | Kimi K2 0905 |
|---|---|---|
| Company | LG AI Research | Moonshot AI |
| Release Date | December 30, 2025 | September 5, 2025 |
| Parameters | 236B | 1.0T |
| Multimodal | No | No |
| Context (input) | 33K | 262K |
| Context (output) | 33K | 262K |
| Input Price / 1M | $0.60 | $0.60 |
| Output Price / 1M | $1.00 | $2.50 |
| Average Score | 0.8 | 0.8 |
| Benchmarks | ||
| MMLU-Pro | 0.8 | 0.8 |
Visual Benchmark Comparison
K-EXAONE-236B-A23B
Kimi K2 0905
MMLU-Pro0.8 vs 0.8
0.8
0.8
Verdict
K-EXAONE-236B-A23B leads in 2 out of 4 comparison categories.
Overall Performance
Both models show comparable average scores: K-EXAONE-236B-A23B — 0.8, Kimi K2 0905 — 0.8.
API Cost
K-EXAONE-236B-A23B is 1.9x cheaper: input $0.60/1M vs $0.60/1M tokens.
Context Window
Kimi K2 0905 supports a larger context: 262K vs 33K tokens.
Recency
K-EXAONE-236B-A23B is newer: released 12/30/2025 vs 9/5/2025.
More About These Models
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Frequently Asked Questions
Which is better for coding — K-EXAONE-236B-A23B or Kimi K2 0905?
Direct comparison on the SWE-Bench benchmark is not available. We recommend reviewing other metrics on the comparison page.
Which model is cheaper — K-EXAONE-236B-A23B or Kimi K2 0905?
K-EXAONE-236B-A23B is cheaper for input: $0.60 per 1M tokens vs $0.60.
Which has a larger context window — K-EXAONE-236B-A23B or Kimi K2 0905?
Kimi K2 0905 supports a larger context: 262,144 tokens vs 32,768.
The K-EXAONE-236B-A23B and Kimi K2 0905 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 Kimi K2 0905 page. See also the complete list of AI model comparisons.