Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Sonnet 4.6
86
Ling 2.6 Flash
44
Pick Claude Sonnet 4.6 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.
Coding
+39.4 difference
Knowledge
+14.7 difference
Claude Sonnet 4.6
Ling 2.6 Flash
$3 / $15
$0.1 / $0.3
44 t/s
209.5 t/s
1.48s
1.07s
200K
262K
Pick Claude Sonnet 4.6 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.
Claude Sonnet 4.6 is clearly ahead on the provisional aggregate, 86 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Sonnet 4.6's sharpest advantage is in coding, where it averages 66.4 against 27. The single biggest benchmark swing on the page is GPQA, 89.9% to 59%.
Claude Sonnet 4.6 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Ling 2.6 Flash. That is roughly 50.0x on output cost alone. Ling 2.6 Flash gives you the larger context window at 262K, compared with 200K for Claude Sonnet 4.6.
Claude Sonnet 4.6 is ahead on BenchLM's provisional leaderboard, 86 to 44. The biggest single separator in this matchup is GPQA, where the scores are 89.9% and 59%.
Claude Sonnet 4.6 has the edge for knowledge tasks in this comparison, averaging 73.7 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Claude Sonnet 4.6 has the edge for coding in this comparison, averaging 66.4 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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