K-EXAONE-236B-A23B vs Llama 3.3 70B Instruct: Specs & Benchmark Comparison
| Characteristic | K-EXAONE-236B-A23B | Llama 3.3 70B Instruct |
|---|---|---|
| Company | LG AI Research | Meta |
| Release Date | December 30, 2025 | December 6, 2024 |
| Parameters | 236B | 70B |
| Multimodal | No | No |
| Context (input) | 33K | 128K |
| Context (output) | 33K | 128K |
| Input Price / 1M | $0.60 | $0.88 |
| Output Price / 1M | $1.00 | $0.88 |
| Average Score | 0.8 | 0.8 |
| Benchmarks | ||
| MMLU-Pro | 0.8 | 0.7 |
Visual Benchmark Comparison
K-EXAONE-236B-A23B
Llama 3.3 70B Instruct
MMLU-Pro0.8 vs 0.7
0.8
0.7
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, Llama 3.3 70B Instruct — 0.8.
API Cost
K-EXAONE-236B-A23B is 1.1x cheaper: input $0.60/1M vs $0.88/1M tokens.
Context Window
Llama 3.3 70B Instruct supports a larger context: 128K vs 33K tokens.
Recency
K-EXAONE-236B-A23B is newer: released 12/30/2025 vs 12/6/2024.
More About These Models
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Frequently Asked Questions
Which is better for coding — K-EXAONE-236B-A23B or Llama 3.3 70B Instruct?
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 Llama 3.3 70B Instruct?
K-EXAONE-236B-A23B is cheaper for input: $0.60 per 1M tokens vs $0.88.
Which has a larger context window — K-EXAONE-236B-A23B or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct supports a larger context: 128,000 tokens vs 32,768.
The K-EXAONE-236B-A23B and Llama 3.3 70B Instruct 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 Llama 3.3 70B Instruct page. See also the complete list of AI model comparisons.