Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek Coder 2.0 and Step 3.5 Flash finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
DeepSeek Coder 2.0 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 Step 3.5 Flash. That is roughly 3.7x on output cost alone. Step 3.5 Flash gives you the larger context window at 256K, compared with 128K for DeepSeek Coder 2.0.
Treat this as a split decision. DeepSeek Coder 2.0 makes more sense if agentic is the priority; Step 3.5 Flash is the better fit if multimodal & grounded is the priority or you want the cheaper token bill.
DeepSeek Coder 2.0
67.5
Step 3.5 Flash
60.2
DeepSeek Coder 2.0
52.8
Step 3.5 Flash
47.1
DeepSeek Coder 2.0
58.6
Step 3.5 Flash
66.7
DeepSeek Coder 2.0
75.5
Step 3.5 Flash
78.3
DeepSeek Coder 2.0
59.6
Step 3.5 Flash
60.8
DeepSeek Coder 2.0
86
Step 3.5 Flash
87
DeepSeek Coder 2.0
79.8
Step 3.5 Flash
82.8
DeepSeek Coder 2.0
80.5
Step 3.5 Flash
84.5
DeepSeek Coder 2.0 and Step 3.5 Flash are tied on overall score, so the right pick depends on which category matters most for your use case.
Step 3.5 Flash has the edge for knowledge tasks in this comparison, averaging 60.8 versus 59.6. 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 47.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for math in this comparison, averaging 84.5 versus 80.5. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for reasoning in this comparison, averaging 78.3 versus 75.5. Inside this category, MuSR 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 60.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for multimodal and grounded tasks in this comparison, averaging 66.7 versus 58.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Step 3.5 Flash 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.
Step 3.5 Flash has the edge for multilingual tasks in this comparison, averaging 82.8 versus 79.8. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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