DeepSeek vs StepFun Step 3.5 Flash

Which one should you pick? Here's the full breakdown.

Our Pick

DeepSeek

A
8.0/10

Near-frontier reasoning for pennies on the dollar -- the open-source LLM that made Silicon Valley nervous

StepFun Step 3.5 Flash

B
7.8/10

StepFun's (China) agent-focused open-weight model -- Step 3.5 Flash launched 2026-02-01. 196B sparse MoE, ~11B active. Benchmarks slightly ahead of DeepSeek V3.2 at over 3x smaller total size. Step 3 (321B / 38B active, Apache 2.0) and Step3-VL-10B multimodal also in the family

CategoryDeepSeekStepFun Step 3.5 Flash
Ease of Use7.56.0
Output Quality8.08.0
Value9.59.0
Features7.08.0
Overall8.07.8

Pricing Comparison

FeatureDeepSeekStepFun Step 3.5 Flash
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

DeepSeek V3.2 benchmarks — StepFun Step 3.5 Flash has no published benchmarks

BenchmarkScore
MMLU90.8%
MMLU-Pro85%
GPQA Diamond79.9%
HumanEval91.5%
SWE-bench67.8%

Which Should You Pick?

Pick DeepSeek if...

  • Easier to use (7.5 vs 6)

Developers and teams who need strong reasoning and coding capabilities on a budget. If you're building AI features and can't justify GPT-4 API costs, DeepSeek is the obvious first stop.

Visit DeepSeek

Pick StepFun Step 3.5 Flash if...

  • More features (8 vs 7)

Teams building agent systems on Chinese open-weight foundations who want something other than DeepSeek or Qwen, especially if agentic tool-use is the primary workload. Also good for Chinese-market products where StepFun's domestic tuning advantages matter. And for anyone looking to add diversity to their open-weight evaluation matrix beyond the top-3 Chinese labs.

Visit StepFun Step 3.5 Flash

Our Verdict

DeepSeek and StepFun Step 3.5 Flash are extremely close overall. Your choice comes down to specific needs -- DeepSeek is better for developers and teams who need strong reasoning and coding capabilities on a budget, while StepFun Step 3.5 Flash works best for teams building agent systems on chinese open-weight foundations who want something other than deepseek or qwen, especially if agentic tool-use is the primary workload.