Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0 finishes one point ahead overall, 66 to 65. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
DeepSeek Coder 2.0's sharpest advantage is in coding, where it averages 52.8 against 42.4. The single biggest benchmark swing on the page is MMMU-Pro, 50 to 80. Seed 1.6 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 is also the more expensive model on tokens at $0.25 input / $2.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek Coder 2.0. Seed 1.6 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 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 only becomes the better choice if multimodal & grounded is the priority or you need the larger 256K context window.
DeepSeek Coder 2.0
67.5
Seed 1.6
62.3
DeepSeek Coder 2.0
52.8
Seed 1.6
42.4
DeepSeek Coder 2.0
58.6
Seed 1.6
79.6
DeepSeek Coder 2.0
75.5
Seed 1.6
74.5
DeepSeek Coder 2.0
59.6
Seed 1.6
56.4
DeepSeek Coder 2.0
86
Seed 1.6
87
DeepSeek Coder 2.0
79.8
Seed 1.6
83.4
DeepSeek Coder 2.0
80.5
Seed 1.6
75.9
DeepSeek Coder 2.0 is ahead overall, 66 to 65. The biggest single separator in this matchup is MMMU-Pro, where the scores are 50 and 80.
DeepSeek Coder 2.0 has the edge for knowledge tasks in this comparison, averaging 59.6 versus 56.4. 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 42.4. Inside this category, HumanEval 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 75.9. 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 74.5. 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 62.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 58.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for instruction following in this comparison, averaging 87 versus 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Seed 1.6 has the edge for multilingual tasks in this comparison, averaging 83.4 versus 79.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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