Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Llama 3.1 405B is clearly ahead on the aggregate, 56 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
K-Exaone is the reasoning model in the pair, while Llama 3.1 405B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. K-Exaone gives you the larger context window at 256K, compared with 128K for Llama 3.1 405B.
Pick Llama 3.1 405B if you want the stronger benchmark profile. K-Exaone only becomes the better choice if coding is the priority or you need the larger 256K context window.
Benchmark data for this category is coming soon.
K-Exaone
49.4
Llama 3.1 405B
46
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Llama 3.1 405B is ahead overall, 56 to 49. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.4% and 46%.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 46. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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