Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Ling 2.6 Flash
44
Qwen3.6-35B-A3B
70
Verified leaderboard positions: Ling 2.6 Flash unranked · Qwen3.6-35B-A3B #15
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+39.9 difference
Knowledge
+1.5 difference
Ling 2.6 Flash
Qwen3.6-35B-A3B
$0.1 / $0.3
N/A
209.5 t/s
N/A
1.07s
N/A
262K
262K
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6-35B-A3B is clearly ahead on the provisional aggregate, 70 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-35B-A3B's sharpest advantage is in coding, where it averages 66.9 against 27. The single biggest benchmark swing on the page is GPQA, 59% to 86%.
Qwen3.6-35B-A3B is the reasoning model in the pair, while Ling 2.6 Flash 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.
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 70 to 44. The biggest single separator in this matchup is GPQA, where the scores are 59% and 86%.
Qwen3.6-35B-A3B has the edge for knowledge tasks in this comparison, averaging 60.5 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.6-35B-A3B has the edge for coding in this comparison, averaging 66.9 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.