Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
K-Exaone is clearly ahead on the aggregate, 49 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
K-Exaone's sharpest advantage is in coding, where it averages 49.4 against 28.3. The single biggest benchmark swing on the page is SWE-bench Verified, 49.2% to 49.4%.
K-Exaone gives you the larger context window at 256K, compared with 128K for DeepSeek-R1.
Pick K-Exaone if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
DeepSeek-R1
28.3
K-Exaone
49.4
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.
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.
K-Exaone is ahead overall, 49 to 45. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.2% and 49.4%.
K-Exaone has the edge for coding in this comparison, averaging 49.4 versus 28.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.