Mistral AI vs StepFun Step 3.5 Flash

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

Mistral AI

B
7.5/10

European AI lab with open and commercial models -- Mistral Small 4 (Mar 2026, 119B MoE Apache 2.0 unified model), Medium 3 (Apr 9 2026), and Voxtral TTS (open-source speech, Mar 2026)

Our Pick

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

CategoryMistral AIStepFun Step 3.5 Flash
Ease of Use6.06.0
Output Quality8.08.0
Value9.09.0
Features7.08.0
Overall7.57.8

Pricing Comparison

FeatureMistral AIStepFun Step 3.5 Flash
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

Mistral Large 3 / Small 4 benchmarks — StepFun Step 3.5 Flash has no published benchmarks

BenchmarkScore
MMLU86%
HumanEval92%
MATH69%

Which Should You Pick?

Pick Mistral AI if...

Developers who want cheap, high-quality API access. Also strong for multilingual applications and European companies that prefer an EU-based AI provider for data residency.

Visit Mistral AI

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

Mistral AI and StepFun Step 3.5 Flash are extremely close overall. Your choice comes down to specific needs -- Mistral AI is better for developers who want cheap, high-quality api access, 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.