Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Ling 2.6 Flash
44
o1
59
Pick o1 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you want the cheaper token bill or you need the larger 262K context window.
Knowledge
+16.7 difference
Inst. Following
+35.2 difference
Ling 2.6 Flash
o1
$0.1 / $0.3
$15 / $60
209.5 t/s
98 t/s
1.07s
32.29s
262K
200K
Pick o1 if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you want the cheaper token bill or you need the larger 262K context window.
o1 is clearly ahead on the provisional aggregate, 59 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1's sharpest advantage is in instruction following, where it averages 92.2 against 57. The single biggest benchmark swing on the page is GPQA, 59% to 75.7%.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Ling 2.6 Flash. That is roughly 200.0x on output cost alone. o1 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. Ling 2.6 Flash gives you the larger context window at 262K, compared with 200K for o1.
o1 is ahead on BenchLM's provisional leaderboard, 59 to 44. The biggest single separator in this matchup is GPQA, where the scores are 59% and 75.7%.
o1 has the edge for knowledge tasks in this comparison, averaging 75.7 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
o1 has the edge for instruction following in this comparison, averaging 92.2 versus 57. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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