Step-3.5-Flash
Step 3.5 Flash is a 196B parameter sparse Mixture-of-Experts (MoE) foundation model from StepFun that activates ~11B parameters per token. It targets reasoning, coding, and agentic workloads with a 256K context window, achieving 100–350 tokens/second throughput via Multi-Token Prediction. The model is Apache 2.0 licensed, ungated, and available via OpenRouter and StepFun's API, as well as downloadable for local deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | stepfun-ai |
| Parameters | 199.4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 175.3k |
| Likes | 824 |
| Last updated | 2026-03-17 |
| Source | stepfun-ai/Step-3.5-Flash |
What Step-3.5-Flash is
Step 3.5 Flash is a 45-layer transformer with 4,096 hidden dimensions, 128,896 token vocabulary, and sparse MoE routing (288 experts per layer, Top-8 selection). It supports 256K context via hybrid 3:1 Sliding Window Attention, generates 4 tokens per forward pass (MTP-3), and is optimized for inference efficiency on consumer to enterprise hardware. Model card provides arxiv references (2602.10604, 2601.05593, 2507.19427) for architecture and evaluation methodology.
Run Step-3.5-Flash locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="stepfun-ai/Step-3.5-Flash")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: ~40–60 GB VRAM for full-precision inference on a single GPU (11B active params per token); quantized (int8/int4) deployment likely requires 20–30 GB. Card mentions M4 Max and NVIDIA DGX Spark; precise memory footprint and multi-GPU requirements are not stated. Requires review of official deployment guides for accuracy.
Model card does not document LoRA, QLoRA, or instruction-tuning feasibility. MoE routing and multi-token prediction architecture may require specialized fine-tuning frameworks. Requires review of StepFun's fine-tuning documentation or community forks (GitHub noted in card).
When to avoid it — and what to weigh
- Latency-critical applications requiring sub-100ms inference on resource-constrained devices — While fast relative to dense 196B models, absolute latency numbers are not provided. Deployment on edge/mobile devices is not mentioned as supported.
- Applications requiring real-time multilingual support across low-resource languages — Vocabulary (128,896 tokens) and benchmarks focus on English and Chinese. Coverage for other languages is not documented.
- Production deployments where model context-window stability is critical — Card notes 3:1 SWA hybrid approach for long context; full evaluation of context window boundaries and potential degradation at limits is not detailed.
- Regulatory environments requiring audited model training data provenance or bias mitigation documentation — Model card does not disclose training data composition, filtering, or formal bias evaluation results.
License & commercial use
Apache License 2.0 (permissive OSI license). Permits commercial use, modification, and distribution under Apache 2.0 terms (attribution and NOTICE file required). No additional proprietary restrictions mentioned in card.
Apache 2.0 is a standard permissive OSI license explicitly allowing commercial use. Model is ungated. However, API access via OpenRouter or StepFun platform entails separate commercial terms (verify with each provider). Self-hosted deployment for commercial purposes is permitted under Apache 2.0 provided license terms are retained.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Card does not provide security audit, adversarial robustness evaluation, or documented mitigations for jailbreaking or prompt injection. MoE architecture may present novel attack surfaces (expert routing). Local deployment claim emphasizes data privacy but does not detail model safety measures. Recommend reviewing third-party red-teaming reports and conducting internal security evaluation before production deployment.
Alternatives to consider
DeepSeek V3.2
Similar MoE sparse activation (37B active from 671B) with comparable reasoning benchmarks (AIME 93.1 vs. Step 97.3). Larger expert pool and slower generation (33 tok/s). Consider if maximum reasoning depth outweighs latency.
GLM-4.7
Dense 32B activation, similar generation speed (100 tok/s) and context support (256K). Simpler to serve and fine-tune; lower reasoning on AIME (95.7 vs. 97.3) and SWE-bench (73.8 vs. 74.4). Consider if deployment simplicity is valued over sparse efficiency.
MiniMax M2.1
Smaller MoE footprint (10B active from 230B), comparable speed (100 tok/s). Lower reasoning benchmarks (AIME 83.0, HMMT 2025 Feb 71.0) but lower compute cost. Consider for budget-constrained deployments prioritizing speed over reasoning depth.
Ship Step-3.5-Flash with senior software developers
Assess your hardware requirements, evaluate local vs. API deployment, and pilot with OpenRouter's free tier. Contact our AI engineering team to design agentic workflows and optimize inference for your workload.
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Step-3.5-Flash FAQ
Can I use Step 3.5 Flash commercially in a self-hosted setup?
What GPU do I need to run Step 3.5 Flash locally?
Does Step 3.5 Flash support fine-tuning or instruction-tuning?
How does Step 3.5 Flash compare to GPT-4 or Claude 3.5 in real-world agent tasks?
Software developers & web developers for hire
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Step-3.5-Flash is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Step 3.5 Flash?
Assess your hardware requirements, evaluate local vs. API deployment, and pilot with OpenRouter's free tier. Contact our AI engineering team to design agentic workflows and optimize inference for your workload.