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Step 3.5 Flash

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OpenClaw Guide  |  Claude Code Guide  |  Roo Code Guide  |  Local Agent Guide

Hugging Face ModelScope Discord Paper Webpage License Chat with the model on OpenRouter Chat with the model on HuggingfaceSpace OpenClaw Integration

1. Introduction

Step 3.5 Flash (visit website) is our most capable open-source foundation model, engineered to deliver frontier reasoning and agentic capabilities with exceptional efficiency. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. This "intelligence density" allows it to rival the reasoning depth of top-tier proprietary models, while maintaining the agility required for real-time interaction.

Contents

2. Key Capabilities

  • Deep Reasoning at Speed: While chatbots are built for reading, agents must reason fast. Powered by 3-way Multi-Token Prediction (MTP-3), Step 3.5 Flash achieves a generation throughput of 100–300 tok/s in typical usage (peaking at 350 tok/s for single-stream coding tasks). This allows for complex, multi-step reasoning chains with immediate responsiveness.

  • A Robust Engine for Coding & Agents: Step 3.5 Flash is purpose-built for agentic tasks, integrating a scalable RL framework that drives consistent self-improvement. It achieves 74.4% on SWE-bench Verified and 51.0% on Terminal-Bench 2.0, proving its ability to handle sophisticated, long-horizon tasks with unwavering stability.

  • Efficient Long Context: The model supports a cost-efficient 256K context window by employing a 3:1 Sliding Window Attention (SWA) ratio—integrating three SWA layers for every full-attention layer. This hybrid approach ensures consistent performance across massive datasets or long codebases while significantly reducing the computational overhead typical of standard long-context models.

  • Accessible Local Deployment: Optimized for accessibility, Step 3.5 Flash brings elite-level intelligence to local environments. It runs securely on high-end consumer hardware (e.g., Mac Studio M4 Max, NVIDIA DGX Spark), ensuring data privacy without sacrificing performance.

3. Performance

Step 3.5 Flash delivers performance parity with leading closed-source systems while remaining open and efficient.

Performance of Step 3.5 Flash measured across Reasoning, Coding, and Agentic Capabilities. Open-source models (left) are sorted by their total parameter count, while top-tier proprietary models are shown on the right. xbench-DeepSearch scores are sourced from official publications for consistency. The shadowed bars represent the enhanced performance of Step 3.5 Flash using Parallel Thinking.

Detailed Benchmarks

Benchmark Step 3.5 Flash DeepSeek V3.2 Kimi K2 Thinking / K2.5 GLM-4.7 MiniMax M2.1 MiMo-V2 Flash
# Activated Params 11B 37B 32B 32B 10B 15B
# Total Params (MoE) 196B 671B 1T 355B 230B 309B
Est. decoding cost @ 128K context, Hopper GPU** 1.0x
100 tok/s, MTP-3, EP8		 6.0x
33 tok/s, MTP-1, EP32		 18.9x
33 tok/s, no MTP, EP32		 18.9x
100 tok/s, MTP-3, EP8		 3.9x
100 tok/s, MTP-3, EP8		 1.2x
100 tok/s, MTP-3, EP8
Agent
τ²-Bench 88.2 80.3 (85.2*) 74.3*/85.4* 87.4 86.6* 80.3 (84.1*)
BrowseComp 51.6 51.4 41.5* / 60.6 52.0 47.4 45.4
BrowseComp (w/ Context Manager) 69.0 67.6 60.2/74.9 67.5 62.0 58.3
BrowseComp-ZH 66.9 65.0 62.3 / 62.3* 66.6 47.8* 51.2*
BrowseComp-ZH (w/ Context Manager) 73.7 — —/— — — —
GAIA (no file) 84.5 75.1* 75.6*/75.9* 61.9* 64.3* 78.2*
xbench-DeepSearch (2025.05) 83.7 78.0* 76.0*/76.7* 72.0* 68.7* 69.3*
xbench-DeepSearch (2025.10) 56.3 55.7* —/40+ 52.3* 43.0* 44.0*
ResearchRubrics 65.3 55.8* 56.2*/59.5* 62.0* 60.2* 54.3*
Reasoning
AIME 2025 97.3 93.1 94.5/96.1 95.7 83.0 94.1 (95.1*)
HMMT 2025 (Feb.) 98.4 92.5 89.4/95.4 97.1 71.0* 84.4 (95.4*)
HMMT 2025 (Nov.) 94.0 90.2 89.2*/— 93.5 74.3* 91.0*
IMOAnswerBench 85.4 78.3 78.6/81.8 82.0 60.4* 80.9*
Coding
LiveCodeBench-V6 86.4 83.3 83.1/85.0 84.9 — 80.6 (81.6*)
SWE-bench Verified 74.4 73.1 71.3/76.8 73.8 74.0 73.4
Terminal-Bench 2.0 51.0 46.4 35.7*/50.8 41.0 47.9 38.5

Notes

  • "—" indicates the score is not publicly available or not tested.
  • "*" indicates the original score was inaccessible or lower than our reproduced, so we report the evaluation under the same test conditions as Step 3.5 Flash to ensure fair comparability.
  • BrowseComp (with Context Manager): when the effective context length exceeds a predefined threshold, the agent resets the context and restarts the agent loop. (By contrast, Kimi K2.5 and DeepSeek-V3.2 used a discard-all strategy.)
  • In decoding cost section, decoding is estimated using a similar but more accurate approach than arxiv.org/abs/2507.19427.

4. Architecture Details

Step 3.5 Flash is built on a Sparse Mixture-of-Experts (MoE) transformer architecture, optimized for high throughput and low VRAM usage during inference.

4.1 Technical Specifications

Component Specification
Backbone 45-layer Transformer (4,096 hidden dim)
Context Window 256K
Vocabulary 128,896 tokens
Total Parameters 196.81B (196B Backbone + 0.81B Head)
Active Parameters ~11B (per token generation)

4.2 Mixture of Experts (MoE) Routing

Unlike traditional dense models, Step 3.5 Flash uses a fine-grained routing strategy to maximize efficiency:

  • Fine-Grained Experts: 288 routed experts per layer + 1 shared expert (always active).
  • Sparse Activation: Only the Top-8 experts are selected per token.
  • Result: The model retains the "memory" of a 196B parameter model but executes with the speed of an 11B model.

4.3 Multi-Token Prediction (MTP)

To improve inference speed, we utilize a specialized MTP Head consisting of a sliding-window attention mechanism and a dense Feed-Forward Network (FFN). This module predicts 4 tokens simultaneously in a single forward pass, significantly accelerating inference without degrading quality.

5. Quick Start

You can get started with Step 3.5 Flash in minutes using Cloud API via our supported providers.

5.1 OpenRouter

OpenRouter provides uniform access to Step 3.5 Flash with both free and paid tiers.

Models:

Configuration:

  • Base URL: https://openrouter.ai/api/v1
  • API Key: Sign up at OpenRouter to get your key.

5.2 StepFun Platform

StepFun offers official API endpoints for both International and Chinese users.

Region Website Base URL
International platform.stepfun.ai https://api.stepfun.ai/v1
China platform.stepfun.com https://api.stepfun.com/v1

Note: China platform requires +86 phone number verification.

5.3 OpenClaw (Recommended)

For a full agentic experience, we recommend using OpenClaw. 👉 View the OpenClaw Cookbook to get started in minutes.

5.4 Implementation Example

Install the standard OpenAI SDK (compatible with both platforms):

pip install --upgrade "openai>=1.0"

Python Example:

from openai import OpenAI

# Configuration for OpenRouter
# base_url = "https://openrouter.ai/api/v1"
# api_key = "sk-or-..." 
# model = "stepfun/step-3.5-flash"

# Configuration for StepFun (International)
base_url = "https://api.stepfun.ai/v1" 
# For China: base_url = "https://api.stepfun.com/v1"
api_key = "your-stepfun-api-key"
model = "step-3.5-flash"

client = OpenAI(api_key=api_key, base_url=base_url)

completion = client.chat.completions.create(
    model=model,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello! Introduce yourself."}
    ]
)

print(completion.choices[0].message.content)

6. Local Deployment

Step 3.5 Flash is optimized for local inference and supports industry-standard backends including vLLM, SGLang, Hugging Face Transformers and llama.cpp.

6.1 vLLM

We recommend using the latest nightly build of vLLM.

  1. Install vLLM.
# via Docker
docker pull vllm/vllm-openai:nightly

# or via pip (nightly wheels)
pip install -U vllm --pre \
  --index-url https://pypi.org/simple \
  --extra-index-url https://wheels.vllm.ai/nightly
  1. Launch the server.

Note: Full MTP3 support is not yet available in vLLM. We are actively working on a Pull Request to integrate this feature, which is expected to significantly enhance decoding performance.

  • For fp8 model
vllm serve <MODEL_PATH_OR_HF_ID> \
  --served-model-name step3p5-flash \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --disable-cascade-attn \
  --reasoning-parser step3p5 \
  --enable-auto-tool-choice \
  --tool-call-parser step3p5 \
  --hf-overrides '{"num_nextn_predict_layers": 1}' \
  --speculative_config '{"method": "step3p5_mtp", "num_speculative_tokens": 1}' \
  --trust-remote-code \
  --quantization fp8
  • For bf16 model
vllm serve <MODEL_PATH_OR_HF_ID> \
  --served-model-name step3p5-flash \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --disable-cascade-attn \
  --reasoning-parser step3p5 \
  --enable-auto-tool-choice \
  --tool-call-parser step3p5 \
  --hf-overrides '{"num_nextn_predict_layers": 1}' \
  --speculative_config '{"method": "step3p5_mtp", "num_speculative_tokens": 1}' \
  --trust-remote-code

6.2 SGLang

  1. Install SGLang.
# via Docker
docker pull lmsysorg/sglang:dev-pr-18084
# or from source (pip)
pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git"
  1. Launch the server.
  • For bf16 model
SGLANG_ENABLE_SPEC_V2=1
sglang serve \
  --model-path <MODEL_PATH_OR_HF_ID> \
  --served-model-name step3p5-flash \
  --tp-size 8 \
  --tool-call-parser step3p5 \
  --reasoning-parser step3p5 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --enable-multi-layer-eagle \
  --host 0.0.0.0 \
  --port 8000
  • For fp8 model
SGLANG_ENABLE_SPEC_V2=1
sglang serve \
  --model-path <MODEL_PATH_OR_HF_ID> \
  --served-model-name step3p5-flash \
  --tp-size 8 \
  --ep-size 8 \
  --tool-call-parser step3p5 \
  --reasoning-parser step3p5 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --enable-multi-layer-eagle \
  --host 0.0.0.0 \
  --port 8000

6.3 Transformers (Debug / Verification)

Use this snippet for quick functional verification. For high-throughput serving, use vLLM or SGLang.

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "<MODEL_PATH_OR_HF_ID>"

# 1. Setup
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)

# 2. Prepare Input
messages = [{"role": "user", "content": "Explain the significance of the number 42."}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

# 3. Generate
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)

print(output_text)

6.4 llama.cpp

System Requirements

  • GGUF Model Weights(int4): 111.5 GB
  • Runtime Overhead: ~7 GB
  • Minimum VRAM: 120 GB (e.g., Mac studio, DGX-Spark, AMD Ryzen AI Max+ 395)
  • Recommended: 128GB unified memory

Notes

Steps

  1. Use official llama.cpp

the folder Step-3.5-Flash/tree/main/llama.cpp is obsolete

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
  1. Build llama.cpp on Mac:
cmake -S . -B build-macos \
  -DCMAKE_BUILD_TYPE=Release \
  -DGGML_METAL=ON \
  -DGGML_ACCELERATE=ON \
  -DLLAMA_BUILD_EXAMPLES=ON \
  -DLLAMA_BUILD_COMMON=ON \
  -DGGML_LTO=ON
cmake --build build-macos -j8
  1. Build llama.cpp on DGX-Spark:
cmake -S . -B build-cuda \
  -DCMAKE_BUILD_TYPE=Release \
  -DGGML_CUDA=ON \
  -DGGML_CUDA_GRAPHS=ON \
  -DLLAMA_CURL=OFF \
  -DLLAMA_BUILD_EXAMPLES=ON \
  -DLLAMA_BUILD_COMMON=ON
cmake --build build-cuda -j8
  1. Build llama.cpp on AMD Windows:
cmake -S . -B build-vulkan \
  -DCMAKE_BUILD_TYPE=Release \
  -DLLAMA_CURL=OFF \
  -DGGML_OPENMP=ON \
  -DGGML_VULKAN=ON
cmake --build build-vulkan -j8
  1. Run with llama-cli
./llama-cli -m step3.5_flash_Q4_K_S.gguf -c 16384 -b 2048 -ub 2048 -fa on --temp 1.0 -p "What's your name?"
  1. Test performance with llama-batched-bench (Benchmark details)
./llama-batched-bench -m step3.5_flash_Q4_K_S.gguf -c 32768 -b 2048 -ub 2048 -npp 0,2048,8192,16384,32768 -ntg 128 -npl 1

7. Using Step 3.5 Flash on Agent Platforms

Note: As mentioned in the Quick Start, you can access Step 3.5 Flash via OpenRouter or the StepFun Platform. Choose the base URL and API key corresponding to your preferred provider when configuring these agents.

7.1 OpenClaw (Recommended)

OpenClaw is a powerful agentic platform that works seamlessly with Step 3.5 Flash. Step 3.5 Flash is a perfect fit for OpenClaw due to its high speed and strong agentic capabilities (maintaining deep reasoning and consistency during execution).

Quick Setup:

  1. Install: curl -fsSL https://openclaw.ai/install.sh | bash
  2. Onboard: Run openclaw onboard.
  3. Configure: In WebUI (Config -> Models), add a new provider:
    • Type: openai-completions
    • Base URL:
      • StepFun: https://api.stepfun.ai/v1 (International) or https://api.stepfun.com/v1 (China)
      • OpenRouter: https://openrouter.ai/api/v1
    • Model ID: step-3.5-flash (or stepfun/step-3.5-flash for OpenRouter)

For a full walkthrough, see our OpenClaw Cookbook.

7.2 Claude Code

You can configure Claude Code to use Step 3.5 Flash by modifying your settings.

Update ~/.claude/settings.json:

{
  "env": {
    "ANTHROPIC_API_KEY": "YOUR_STEPFUN_API_KEY",
    "ANTHROPIC_BASE_URL": "https://api.stepfun.ai/"  // Use "https://api.stepfun.com/" for China endpoint
  },
  "model": "step-3.5-flash"
}

Note: For more advanced routing or OpenAI-style usage, we recommend claude-code-router.

7.3 OpenAI Codex (CLI)

To use Step 3.5 Flash with the @openai/codex CLI, update your ~/.codex/config.toml:

model="step-3.5-flash"
model_provider = "stepfun-chat"
preferred_auth_method = "apikey"

[model_providers.stepfun-chat]
name = "OpenAI using response"
base_url = "https://api.stepfun.ai/v1"
env_key = "OPENAI_API_KEY"
wire_api = "chat"

7.4 Step-DeepResearch

Step 3.5 Flash powers the reasoning core of Step-DeepResearch. To use it, simply set MODEL_NAME to Step-3.5-Flash in your .env file.

See the Step-DeepResearch Repository for full setup instructions.

8. Cookbooks

Explore our Cookbooks directory for practical examples and integration guides, including:

Visit the Cookbooks README for more details and contribution guidelines.

9. Known Issues and Future Directions

  1. Token Efficiency. Step 3.5 Flash achieves frontier-level agentic intelligence but currently relies on longer generation trajectories than Gemini 3.0 Pro to reach comparable quality.
  2. Efficient Universal Mastery. We aim to unify generalist versatility with deep domain expertise. To achieve this efficiently, we are advancing variants of on-policy distillation, allowing the model to internalize expert behaviors with higher sample efficiency.
  3. RL for More Agentic Tasks. While Step 3.5 Flash demonstrates competitive performance on academic agentic benchmarks, the next frontier of agentic AI necessitates the application of RL to intricate, expert-level tasks found in professional work, engineering, and research.
  4. Operational Scope and Constraints. Step 3.5 Flash is tailored for coding and work-centric tasks, but may experience reduced stability during distribution shifts. This typically occurs in highly specialized domains or long-horizon, multi-turn dialogues, where the model may exhibit repetitive reasoning, mixed-language outputs, or inconsistencies in time and identity awareness.

10. Co-Developing the Future

We view our roadmap as a living document, evolving continuously based on real-world usage and developer feedback. As we work to shape the future of AGI by expanding broad model capabilities, we want to ensure we are solving the right problems. We invite you to be part of this continuous feedback loop—your insights directly influence our priorities.

  • Join the Conversation: Our Discord community is the primary hub for brainstorming future architectures, proposing capabilities, and getting early access updates 🚀
  • Report Friction: Encountering limitations? You can open an issue on GitHub or flag it directly in our Discord support channels.

License

This project is open-sourced under the Apache 2.0 License.

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