Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 62 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek LLM 2.0's sharpest advantage is in coding, where it averages 42.9 against 27.6. The single biggest benchmark swing on the page is SWE-bench Verified, 46 to 24. 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.
Seed 1.6 Flash is the reasoning model in the pair, while DeepSeek LLM 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 LLM 2.0.
Pick DeepSeek LLM 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 need the larger 256K context window.
DeepSeek LLM 2.0
57.9
Seed 1.6 Flash
54.5
DeepSeek LLM 2.0
42.9
Seed 1.6 Flash
27.6
DeepSeek LLM 2.0
64.5
Seed 1.6 Flash
73.1
DeepSeek LLM 2.0
73.6
Seed 1.6 Flash
66.8
DeepSeek LLM 2.0
57.5
Seed 1.6 Flash
47.3
DeepSeek LLM 2.0
85
Seed 1.6 Flash
81
DeepSeek LLM 2.0
78.8
Seed 1.6 Flash
72.8
DeepSeek LLM 2.0
80.8
Seed 1.6 Flash
67.1
DeepSeek LLM 2.0 is ahead overall, 62 to 56. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 46 and 24.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 47.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for coding in this comparison, averaging 42.9 versus 27.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for math in this comparison, averaging 80.8 versus 67.1. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for reasoning in this comparison, averaging 73.6 versus 66.8. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for agentic tasks in this comparison, averaging 57.9 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 64.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for instruction following in this comparison, averaging 85 versus 81. Inside this category, IFEval is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for multilingual tasks in this comparison, averaging 78.8 versus 72.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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