Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0 is clearly ahead on the aggregate, 66 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek Coder 2.0's sharpest advantage is in coding, where it averages 52.8 against 27.6. The single biggest benchmark swing on the page is SWE-bench Pro, 61 to 31. Seed 1.6 Flash does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
DeepSeek Coder 2.0 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.08 input / $0.30 output per 1M tokens for Seed 1.6 Flash. That is roughly 3.7x on output cost alone. Seed 1.6 Flash is the reasoning model in the pair, while DeepSeek Coder 2.0 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. Seed 1.6 Flash gives you the larger context window at 256K, compared with 128K for DeepSeek Coder 2.0.
Pick DeepSeek Coder 2.0 if you want the stronger benchmark profile. Seed 1.6 Flash only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
DeepSeek Coder 2.0
67.5
Seed 1.6 Flash
54.5
DeepSeek Coder 2.0
52.8
Seed 1.6 Flash
27.6
DeepSeek Coder 2.0
58.6
Seed 1.6 Flash
73.1
DeepSeek Coder 2.0
75.5
Seed 1.6 Flash
66.8
DeepSeek Coder 2.0
59.6
Seed 1.6 Flash
47.3
DeepSeek Coder 2.0
86
Seed 1.6 Flash
81
DeepSeek Coder 2.0
79.8
Seed 1.6 Flash
72.8
DeepSeek Coder 2.0
80.5
Seed 1.6 Flash
67.1
DeepSeek Coder 2.0 is ahead overall, 66 to 56. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 61 and 31.
DeepSeek Coder 2.0 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 47.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for coding in this comparison, averaging 52.8 versus 27.6. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for math in this comparison, averaging 80.5 versus 67.1. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for reasoning in this comparison, averaging 75.5 versus 66.8. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for agentic tasks in this comparison, averaging 67.5 versus 54.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Seed 1.6 Flash has the edge for multimodal and grounded tasks in this comparison, averaging 73.1 versus 58.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for instruction following in this comparison, averaging 86 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek Coder 2.0 has the edge for multilingual tasks in this comparison, averaging 79.8 versus 72.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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