Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
37
Ling 2.6 Flash
44
Pick Ling 2.6 Flash if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if instruction following is the priority.
Coding
+12.2 difference
Knowledge
+11.0 difference
Inst. Following
+29.1 difference
DeepSeek V3
Ling 2.6 Flash
$0.27 / $1.1
$0.1 / $0.3
N/A
209.5 t/s
N/A
1.07s
128K
262K
Pick Ling 2.6 Flash if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if instruction following is the priority.
Ling 2.6 Flash is clearly ahead on the provisional aggregate, 44 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Ling 2.6 Flash. That is roughly 3.7x on output cost alone. Ling 2.6 Flash gives you the larger context window at 262K, compared with 128K for DeepSeek V3.
Ling 2.6 Flash is ahead on BenchLM's provisional leaderboard, 44 to 37. The biggest single separator in this matchup is GPQA, where the scores are 59.1% and 59%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for coding in this comparison, averaging 39.2 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 versus 57. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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