Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Step 3.5 Flash is clearly ahead on the aggregate, 66 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.5 Flash's sharpest advantage is in mathematics, where it averages 84.5 against 52.5. The single biggest benchmark swing on the page is MuSR, 82 to 40.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Step 3.5 Flash. That is roughly 7.3x on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while Step 3.5 Flash 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. Step 3.5 Flash gives you the larger context window at 256K, compared with 128K for DeepSeek-R1.
Pick Step 3.5 Flash if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if you want the stronger reasoning-first profile.
Step 3.5 Flash
60.2
DeepSeek-R1
44.5
Step 3.5 Flash
47.1
DeepSeek-R1
21.5
Step 3.5 Flash
66.7
DeepSeek-R1
47.5
Step 3.5 Flash
78.3
DeepSeek-R1
50.9
Step 3.5 Flash
60.8
DeepSeek-R1
37.9
Step 3.5 Flash
87
DeepSeek-R1
69
Step 3.5 Flash
82.8
DeepSeek-R1
60.4
Step 3.5 Flash
84.5
DeepSeek-R1
52.5
Step 3.5 Flash is ahead overall, 66 to 43. The biggest single separator in this matchup is MuSR, where the scores are 82 and 40.
Step 3.5 Flash has the edge for knowledge tasks in this comparison, averaging 60.8 versus 37.9. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for coding in this comparison, averaging 47.1 versus 21.5. Inside this category, HumanEval 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 52.5. 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 50.9. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Step 3.5 Flash has the edge for agentic tasks in this comparison, averaging 60.2 versus 44.5. 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 47.5. 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 69. 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 60.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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