StepFun Step 3.7 Flash
B Tier · 7.8/10
StepFun's (China) agent-focused open-weight family -- Step 3.7 Flash (May 28 2026): 198B sparse MoE vision-language model, ~11B active, 256K context, Apache 2.0, ~400 tok/s, SWE-Bench Pro 56.3. Supersedes Step 3.5 Flash (Feb 2026) as the flagship
Score Breakdown
The Good and the Bad
What we like
- +Step 3.5 Flash at 196B total / 11B active beats DeepSeek V3.2 on several mainstream benchmarks at 3x smaller total parameter count -- an efficient architecture result that suggests StepFun's MoE routing is unusually well-tuned for agentic workloads
- +Agent-focused tuning explicitly -- tool use, function calling, and multi-step planning are prioritized in training objectives, not just general chat. For teams building agent systems with Chinese open-weight models, Step 3.5 Flash is a stronger starting point than general-purpose DeepSeek or Qwen base models
- +Fills an important gap in the site's Chinese open-weight roster (already have DeepSeek, Qwen, GLM, Kimi, MiniMax -- StepFun was the notable absence). StepFun has real traction in the China market and growing Western mindshare through OpenRouter
- +Step3-VL-10B multimodal variant provides a small-footprint vision option in the same family -- 10B size is realistic for consumer GPUs and the vision tower is competitive with Qwen3-VL-10B on standard benchmarks
What could be better
- −Smaller Western community than DeepSeek or Qwen -- fewer tutorials, quants, and third-party fine-tunes. Production adoption outside China is still early, which means less Stack Overflow coverage when you hit edge cases
- −PRC content filters apply -- same category of refusals on politically sensitive topics as DeepSeek, Qwen, GLM. Not a dealbreaker for most commercial work but worth flagging
- −Step 3.5 Flash's benchmark wins are strongest on Chinese-heavy evaluations and lighter on purely-English benchmarks versus comparably-sized Western models. English writing quality trails Claude / Mistral / Llama meaningfully
- −Release cadence is fast (Step 3 → Step 3.5 in months) but model naming / versioning is confusing -- expect churn in which specific variant is the 'current best' through Q2 2026
Pricing
Self-hosted (Apache 2.0)
- ✓Apache 2.0 license on Step 3 + Step3-VL-10B
- ✓Weights on Hugging Face (stepfun-ai/step3)
- ✓Agent-focused tuning -- tool use, planning, multi-step reasoning
API (StepFun / OpenRouter)
- ✓Available on OpenRouter and select hosted providers
- ✓StepFun direct API for enterprise customers
- ✓Pricing in line with other mid-tier Chinese open-weight models
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Step 3.5 Flash (196B total / 11B active MoE)Agent-focused tuning; MoE routing activates ~11B params per token | 64 GB RAM + 16 GB GPU (Q3 offload) | 4× H100 FP8 |
| Step 3 (321B total / 38B active, Apache 2.0) | 128 GB RAM + 24 GB GPU (Q3) | 4× H100 FP8 |
| Step3-VL-10B (multimodal) | 12 GB VRAM Q4 (RTX 3060 / 4070) | 1× A100 40 GB FP16 |
Known Issues
- VERSION BUMP (2026-05-28): **Step 3.7 Flash** released -- 198B-parameter sparse MoE vision-language model (HF auto-counter shows 201B; vendor model card says 198B), ~11B active per token, 1.8B vision encoder + 196B language backbone, **256K context**, Apache 2.0 open weights (main + FP8 + NVFP4 + GGUF variants on HuggingFace), up to ~400 tok/s, three selectable reasoning levels, agent-focused. Model-card benchmarks: SWE-Bench Pro 56.3, Terminal-Bench 2.1 59.5, Toolathlon 49.5, ClawEval-1.1 67.1, SimpleVQA-Search 79.2. API pricing: $0.20/M input (cache miss), $0.04/M (cache hit), $1.15/M outputSource: HuggingFace (huggingface.co/stepfun-ai/Step-3.7-Flash model card) · 2026-05-28
- Step 3.5 Flash was released 2026-02-01 with initial quantization issues for Q3 and below -- community Q5 quants are the practical deployment target as of April 2026. Full FP16 weights require significant multi-GPU setup (196B total params even with 11B active)Source: Hugging Face discussions, Reddit r/LocalLLaMA · 2026-02
- Refuses discussion of Tiananmen, Taiwan sovereignty, Xi Jinping, and other politically sensitive topics per PRC regulations -- same content filters as other Chinese open-weight modelsSource: Hugging Face discussions, user reports · 2026-02
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. 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.
Not for
English-first creative writing, non-technical users, or workflows that touch politically sensitive topics (PRC content filters apply). Also not the right pick if you need the absolute best Western-community ecosystem -- Llama, Qwen, or DeepSeek will have more third-party tooling.
Our Verdict
StepFun Step 3.5 Flash (February 2026) is the notable Chinese open-weight release that doesn't come from the top-3 labs (DeepSeek, Alibaba/Qwen, Zhipu/GLM). The agent-focused tuning and MoE efficiency (196B total / 11B active beats DeepSeek V3.2 at 3x larger) give it a real niche for teams building tool-use agents on open weights. Western community adoption is still early but StepFun is growing OpenRouter presence and the Step 3.5 Flash release is a credible first step into broader ecosystem relevance. Worth adding to any Chinese-open-weight shortlist alongside DeepSeek and Qwen.
Sources
- StepFun research: Step 3 (accessed 2026-04-17)
- Hugging Face: stepfun-ai/step3 (accessed 2026-04-17)
- aibase news: Step 3.5 Flash (accessed 2026-04-17)
Explore more StepFun Step 3.7 Flash rankings
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