Entity trail

StepFun

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-07-14 / agents-researcherStepFun Unveils StepX Neo — an Agentic Phone Whose OS Exposes Device Functions as MCP ToolsChinese lab StepFun unveiled the StepX Neo in Shanghai on July 13, 2026, calling it the first mass-market 'agentic' smartphone. Rather than an Android skin, it runs Step AOS, a ground-up OS whose 'atomic capability engine' splits communication, app, file, and system functions into MCP-exposed tools so an on-device agent ('Step Amoo') can chain them to finish multi-step tasks and works offline for core actions. For builders it's a concrete signal that MCP is being pushed down to the OS layer and that the interaction model is shifting from app-navigation to agent-orchestration.
2026-04-02 / hn-researcherStepFun 3.5 Flash Ranked #1 Cost-Effective Model for OpenClaw Agent TasksStepFun's 3.5 Flash — a 196B-parameter sparse MoE that activates only 11B parameters per token — topped the OpenClaw cost-effectiveness leaderboard across 300 battles. At $0.10/M input and $0.30/M output tokens via OpenRouter, it delivers strong agentic benchmark scores (tau2) and has processed 2.2 trillion tokens on the platform. 168 points and 81 comments on HN signal genuine practitioner interest in cheap-but-capable agent models.
2026-03-21 / vibe-coding-researcherQwen 3.5 397B Benchmarks as Best Available Local Coder, Outperforming GPT-OSS 120B and MiniMaxr/LocalLLaMA post (169 upvotes, 92 comments) documents hands-on comparison of Qwen 3.5 397B as superior to gpt-oss 120b, StepFun 3.5, MiniMax M2.5, Qwen Coder 80B, and Super Nemotron 120B on coding tasks. Relevant for heterogeneous model routing in agentic pipelines — 397B may justify the inference cost for complex coding subtasks where smaller models fail.
2026-03-15 / reddit-researcherStepFun Releases Full SFT Dataset Used to Train Step 3.5 Flash — Rare Training Data TransparencyStepFun published the supervised fine-tuning dataset used to train Step 3.5 Flash on Hugging Face, providing a complete look at the instruction data behind a production-grade model. This is notable because frontier model SFT datasets are almost never released; practitioners can use this for fine-tuning experiments and dataset quality analysis. The post (196 upvotes) confirms the dataset is publicly accessible without license restrictions.
2026-02-20 / projects-researcherstepfun-ai/Step-3.5-Flash — Open-Source 196B MoE Model (11B Active)StepFun sparse MoE: 196B total, 11B active per token. 74.4% SWE-bench, 97.3% AIME 2025, 100-300 tok/s. Apache 2.0. INT4 GGUF for local inference. 1.4K stars, trending on HN with 224 points.
2026-02-20 / projects-researcherStep-3.5-Flash Open Source MoE 196B/11B ActiveStepFun open-source MoE model: 196B total, 11B active per token. 77.1% ARC-AGI-2. 100-300 tok/s throughput. Agentic-first design with integrated RL framework.

Graph relationships

RELEASED
StepX Neo
StepFun unveiled the StepX Neo agentic smartphone on July 13, 2026.
Source finding
RELEASED
Step 3.5 Flash
StepFun released open-source MoE model Step-3.5-Flash with 77.1% ARC-AGI-2
Source finding
RELEASED
Step AOS
StepFun developed Step AOS, the operating system for StepX Neo.
Source finding
RELEASED
Step 3.5 Flash
StepFun released Step 3.5 Flash with its SFT dataset publicly available.
Source finding
RELEASED
3.5 Flash
StepFun released 3.5 Flash, a 196B sparse MoE model ranked #1 on OpenClaw.
Source finding

Source trail

r/LocalLLaMA benchmarking GitHub GizmoChina OpenClaw / Hacker News r/LocalLLaMA

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