Step-5-Preview

📖 Table of Contents

• Introduction
• Key Features
• Model Architecture
• Model Specifications
• Training Data
• Benchmark Results
• Agentic Capabilities
• Real-World Use Cases
• Quickstart
• Deployment
• Evaluation
• Limitations
• Ethical Considerations
• Hardware Requirements
• Performance Metrics
• Citation
• License
• Contact

🚀 Introduction

Step-5-Preview is StepFun's flagship foundation model, designed from the ground up for real-world agentic tasks. It targets professional domains such as AI coding, software engineering, professional knowledge work, and financial analysis.

StepFun's core philosophy for Step 5 is the "Pareto Frontier" — achieving the optimal balance between intelligence and cost. While previous scaling efforts focused on trading more compute for stronger intelligence, the next phase requires improving the efficiency of converting compute into intelligence.

Step-5-Preview represents a generational leap, with StepFun skipping the entire Step 4.x line entirely, going directly from Step-3.7-Flash to Step 5. This decision reflects the magnitude of improvement achieved in this release.

✨ Key Features

• Sparse Mixture-of-Experts (MoE): 600B total parameters, 27B active per token (~4.5% sparsity).
• 1M-Token Context Window: Equivalent to ~1,500 A4 pages, enabled by Sparse GQA.
• Multimodal Input: Text, image, and video (MP4, QuickTime, Matroska; ≤128 MB; ≤5 min recommended).
• Configurable Reasoning Effort: low, medium, high / xhigh.
• Parallel Tool Calling: Natively supported for agentic workflows.
• Strict JSON Schema Output: Reliable integration into structured systems.
• OpenAI-Compatible API: Available via Step API and third-party gateways.
• Open Weights: BF16 checkpoint available now under TypeSafeAI/Step-5-Preview-BF16.

🏗️ Model Architecture

92-Layer "Narrow but Deep" Design

Step-5-Preview uses a 92-layer Transformer with a narrow-deep configuration. This design is specifically intended to create longer information propagation paths for implicit multi-hop reasoning during long prefill operations.

Sparse Grouped-Query Attention (GQA) with Block-Wise Token Merging

To handle the 1M-token context window efficiently, Step-5-Preview introduces Sparse GQA with block-wise token merging. This mechanism uses sparse indexing to select only historical information relevant to the current task, reducing the number of tokens that actually enter attention computation. StepFun states this cuts indexer and top-k selection costs to approximately one-eighth of a denser baseline.

Multimodal Encoder

The model incorporates a unified multimodal encoder that processes text, images, and video frames into a shared latent space. Video is sampled at adaptive frame rates and encoded with temporal attention, allowing the model to understand motion and long-range dependencies in screen recordings, demonstrations, and real-world footage.

📋 Model Specifications

📚 Training Data

Step-5-Preview was trained on a massive, carefully curated corpus spanning:

• Code repositories from multiple languages (Python, C++, Rust, JavaScript, Go, etc.)
• Technical documentation, API references, and software engineering forums
• Scientific papers in computer science, mathematics, physics, and finance
• Financial reports, earnings calls, and market analyses
• Multimodal data including screenshots, UI mockups, video tutorials, and screen recordings
• Agentic trajectories from simulated and real tool-use environments

The data mixture was optimized for long-horizon reasoning and tool use, with a strong emphasis on real-world professional tasks. All data was filtered for quality, safety, and license compliance. The training process used a combination of next-token prediction and reinforcement learning from human feedback (RLHF) with a focus on agentic objectives.

📊 Benchmark Results

Artificial Analysis Intelligence Index

Overall Score: 44 (Intelligence Index v4.3.2, recalibrated September 7, 2026)

This places Step-5-Preview among the top three open-weight models globally, on par with models like Kimi K3 Max (approximately 5× larger at 2.8T parameters) and Qwen3.8 Max. The index covers 10 evaluations including AA-Briefcase, GDPval-AA v2, Terminal-Bench 4.0, SciCode, and Humanity's Last Exam.

Detailed Benchmark Scores

Benchmark Takeaways

🤖 Agentic Capabilities

24-Hour Autonomous GPU Kernel Optimization

In a landmark demonstration of sustained agentic execution, Step-5-Preview was tasked with autonomously optimizing an H100 GPU kernel for up to 24 consecutive hours. The model:

• Independently modified code
• Ran tests and compared results
• Iterated based on performance outcomes
• Reached 508 TFLOPS after approximately 22 hours

For comparison, Claude Opus 5 achieved 493 TFLOPS in the same experiment. This demonstrates Step-5-Preview's ability to sustain productive work over extended periods without human intervention.

Automated Post-Training Experiments

In another 24-hour experiment, Step-5-Preview autonomously improved the accuracy of Qwen3-30B-A3B on AIME24 from 53.3% to 60% through automated post-training experiments. This showcases the model's capacity for self-directed research and optimization.

Long-Horizon Agent Workflows

The model is specifically optimized for agent workflows that require:

• Searching and information retrieval
• Running code and processing tool returns
• Multi-turn tool calls with sustained execution
• Iterative refinement based on intermediate results
• Self-correction and error recovery over thousands of steps

💼 Real-World Use Cases

StepFun demonstrated the model's capabilities across several complex, real-world projects:

• ESP32 Development Board Modifications: Executed development tasks for over 3 hours, demonstrating hardware programming capabilities.
• Front-End Design with 3D Asset Generation: Full-stack development workflows including visual design.
• Full-Process Financial Research: End-to-end investment research workflows, from data gathering to report generation.
• Software Engineering: Comprehensive coding tasks beyond traditional code generation, including front-end, visual development, and programmable hardware scenarios.
• Autonomous Research Assistant: Capable of reading papers, running experiments, and summarizing findings.
• Customer Support Automation: Handles multi-turn conversations with tool calls to internal systems.

⚡ Quickstart

Installation

pip install transformers>=4.56.0
pip install torch>=2.4.0
pip install accelerate

For video/image support:

pip install av pillow

Basic Usage with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "TypeSafeAI/Step-5-Preview-BF16"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype="bfloat16",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain the significance of the Pareto Frontier in AI scaling."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=1024,
    temperature=0.7,
    top_p=0.95,
    reasoning_effort="high",  # low / medium / high / xhigh
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

Multimodal (Image + Video) Usage

from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://example.com/image.jpg"},
            {"type": "video", "url": "https://example.com/video.mp4"},
            {"type": "text", "text": "Describe the scene and summarize the video."},
        ],
    }
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
# ... generate as above

Tool Calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        },
    }
]

messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]

inputs = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256, reasoning_effort="medium")
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

🚢 Deployment

vLLM

vllm serve TypeSafeAI/Step-5-Preview-BF16 \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --max-model-len 1000000 \
  --enable-reasoning \
  --reasoning-parser stepfun

SGLang

python -m sglang.launch_server \
  --model-path TypeSafeAI/Step-5-Preview-BF16 \
  --trust-remote-code \
  --tp 8 \
  --context-length 1000000 \
  --reasoning-parser stepfun

OpenAI-Compatible API

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_STEP_API_KEY",
    base_url="https://api.stepfun.com/v1",
)

response = client.chat.completions.create(
    model="step-5-preview",
    messages=[{"role": "user", "content": "Write a Python function to merge two sorted lists."}],
    reasoning_effort="high",
    max_tokens=2048,
)

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

📈 Evaluation

Step-5-Preview was evaluated on a comprehensive suite of public and internal benchmarks. All evaluations used the model's high reasoning effort setting unless otherwise noted.

⚠️ Limitations

• Knowledge Cutoff: The model's knowledge is current up to mid-2026. It may not be aware of events after that date.
• Hallucination: Like all large language models, Step-5-Preview can generate plausible but incorrect information, especially in domains with sparse training data.
• Long Context Degradation: While the model supports 1M tokens, performance may degrade for extremely long contexts beyond 500K tokens in certain tasks.
• Tool Use Reliability: Tool calling is highly capable but not infallible. Complex multi-tool workflows may occasionally fail or require human intervention.
• Multimodal Limitations: Video understanding is limited to clips under 5 minutes and 128 MB. Extremely high-resolution images may be downscaled.
• Language Coverage: While multilingual, the model is primarily optimized for English and Chinese. Performance in other languages may vary.

⚖️ Ethical Considerations

StepFun is committed to the responsible development and deployment of AI. We have taken the following measures:

• Safety Alignment: The model was fine-tuned with RLHF to refuse harmful requests and promote helpful, honest, and harmless behavior.
• Bias Mitigation: Training data was filtered to reduce harmful stereotypes and biases. However, residual biases may exist.
• Transparency: We provide detailed model cards and benchmark results to enable informed use.
• License Restrictions: The StepFun Community License prohibits certain high-risk uses, including autonomous weapons, surveillance, and malicious cyber activities.
• Content Provenance: We encourage users to clearly label AI-generated content and to use the model ethically.

We urge all users to consider the ethical implications of their applications and to implement appropriate safeguards.

🖥️ Hardware Requirements

For inference with 1M context, additional memory is required for KV cache. We recommend using paged attention and offloading techniques available in vLLM and SGLang.

⚡ Performance Metrics

Measured on 8× H100 80GB with vLLM, batch size 1, BF16.

📚 Citation

If you use Step-5-Preview in your research, please cite:

@misc{stepfun2026step5preview,
  title        = {Step-5-Preview: A 600B Sparse MoE Foundation Model for Real-World Agentic Work},
  author       = {StepFun Team},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/TypeSafeAI/Step-5-Preview-BF16}},
  note         = {Released September 20, 2026}
}

📜 License

Step-5-Preview is released under the StepFun Community License. See the LICENSE file for full terms.

📬 Contact

• Hugging Face: TypeSafeAI
• GitHub: github.com/stepfun-ai
• Discord: Join our Discord
• Email: opensource@stepfun.com
• Website: stepfun.com