Step3-VL-10B - SGLang Documentation

1. Model Introduction

Step3-VL-10B is a lightweight open-source multimodal model developed by StepFun, designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. Despite its compact 10B parameter footprint, Step3-VL-10B excels in visual perception, complex reasoning, and human-centric alignment. Key highlights of Step3-VL-10B include:

STEM Reasoning: Achieves 94.43% on AIME 2025 and 75.95% on MathVision (with PaCoRe), demonstrating exceptional complex reasoning capabilities that outperform models 10×–20× larger.
Visual Perception: Records 92.05% on MMBench and 80.11% on MMMU, establishing strong general visual understanding and multimodal reasoning.
GUI & OCR: Delivers state-of-the-art performance on ScreenSpot-V2 (92.61%), ScreenSpot-Pro (51.55%), and OCRBench (86.75%), optimized for agentic and document understanding tasks.
Spatial Understanding: Demonstrates emergent spatial awareness with 66.79% on BLINK and 57.21% on All-Angles-Bench, establishing strong potential for embodied intelligence applications.

For more details, please refer to the Step3-VL-10B model card on Hugging Face.

2. SGLang Installation

SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements. Please refer to the official SGLang installation guide for installation instructions.

3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

3.1 Basic Configuration

Step3-VL-10B is a compact 10B dense model that can run on a single GPU. Recommended starting configurations vary depending on hardware. Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and quantization method. SGLang supports serving Step3-VL-10B on NVIDIA B200, H200, H100, and AMD MI355X, MI325X, MI300X GPUs.

3.2 Configuration Tips

Single GPU Deployment: Step3-VL-10B fits comfortably on a single GPU with BF16 precision, no tensor parallelism required.
Memory Management: Set lower --context-length to conserve memory if needed. A value of 32768 is sufficient for most scenarios.
FP8 Quantization: Use FP8 quantization to further reduce memory usage while maintaining quality.

4. Model Invocation

4.1 Basic Usage

For basic API usage and request examples, please refer to:

4.2 Advanced Usage

Step3-VL-10B supports image inputs. Here’s a basic example with image input:

Example

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"
                }
            },
            {
                "type": "text",
                "text": "Read all the text in the image."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    max_tokens=2048,
    extra_body={"top_k": -1}
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

Example output:

Output

Response costs: 5.89s
Generated text: Auntie Anne's

CINNAMON SUGAR
1 × 17,000               17,000

SUB TOTAL                    17,000

GRAND TOTAL                 17,000

CASH IDR                    20,000

CHANGE DUE                 3,000

Multi-Image Input Example: Step3-VL-10B can process multiple images in a single request for comparison or analysis:

Example

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://www.civitatis.com/f/china/hong-kong/guia/taxi.jpg"
                }
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://cdn.cheapoguides.com/wp-content/uploads/sites/7/2025/05/GettyImages-509614603-1280x600.jpg"
                }
            },
            {
                "type": "text",
                "text": "Compare these two images and describe the differences in 100 words or less."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    max_tokens=2048,
    extra_body={"top_k": -1}
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

Example Output:

Output

Response costs: 3.24s
Generated text: First image: Single red Hong Kong taxi close - up, clear license plate (RX 5004), “4 SEATS” sticker, urban street with shops behind. Second image: Aerial view of many taxis (red, green) on a highway with a viaduct, some hoods open, dense arrangement. Differences: Scale (single vs many), perspective (close - up vs aerial), context (street shops vs highway), and taxi conditions (normal vs some open hoods).

4.2.2 Reasoning Parser

Step3-VL-10B supports reasoning mode. Enable the reasoning parser during deployment to separate the thinking and content sections:

Command

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --reasoning-parser deepseek-r1 \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

Streaming with Thinking Process:

Example

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Enable streaming to see the thinking process in real-time
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    temperature=0.7,
    max_tokens=2048,
    stream=True,
    extra_body={"top_k": -1}
)

# Process the stream
has_thinking = False
has_answer = False
thinking_started = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print answer content
        if delta.content:
            # Close thinking section and add content header
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()

Example Output:

Output

=============== Thinking =================
Okay, let's see. The problem is asking for 15% of 240. Hmm, I need to remember how to calculate percentages. So, percentage means "per hundred," right? So, 15% is the same as 15 per 100, or 15/100. To find a percentage of a number, I think you convert the percentage to a decimal and then multiply it by the number. Let me check that.

First, 15% as a decimal. To convert a percentage to a decimal, you divide by 100. So 15 divided by 100 is 0.15. Yeah, that's right. So 15% is 0.15 in decimal form. Then, to find 15% of 24
0, I need to multiply 0.15 by 240. Let me do that calculation.

Let me write it out: 0.15 * 240. Let's compute that. Maybe break it down. 0.1 is 10%, and 0.05 is 5%, so 10% of 240 is 24, and 5% of 240 is 12. Then 10% + 5% is 15%, so 24 + 12 is 36. Oh,
 that's a good way to check. So 15% is 10% plus 5%, which adds up to 36. Let me verify with the decimal method. 0.15 * 240. Let's multiply 240 by 0.1 first: 24. Then 240 by 0.05: 12. Adding them gives 36. Yep, that matches. Alternatively, 240 * 15 = 3600, and then divide by 100 (since it's per hundred), so 3600 / 100 = 36. That's another way. So 15% of 240 is 36. Let me make sure I didn't make a mistake. Let's check with another method. 10% of 240 is 24, 20% would be 48, so 15% is halfway between 10% and 20%, which is (24 + 48)/2 = 36. Yep, that works too
. So all methods point to 36. I think that's the answer.

=============== Content =================

To solve the problem "What is 15% of 240?" step by step:

---

### **Step 1: Understand the concept of percentage**
A percentage represents a portion of a whole. Specifically, "percent" means "per hundred." So, 15% means **15 out of 100**, or **15/100**.

---

### **Step 2: Convert the percentage to a decimal**
To use percentages in calculations, convert them to decimals by dividing by 100:
$$
15\% = \frac{15}{100} = 0.15
$$

---

### **Step 3: Multiply the decimal by the given number**
Now, multiply 0.15 (the decimal form of 15%) by 240:
$$
0.15 \times 240 = 36
$$

---

### **Alternative Verification Methods**

#### **Method A: Break into parts**
- 10% of 240 = $ 0.10 \times 240 = 24 $
- 5% of 240 = $ 0.05 \times 240 = 12 $
- Add them: $ 24 + 12 = 36 $

#### **Method B: Use direct multiplication**
- $ 15\% \text{ of } 240 = \frac{15}{100} \times 240 = \frac{3600}{100} = 36 $

#### **Method C: Estimate using known percentages**
- 20% of 240 = $ 0.20 \times 240 = 48 $
- 10% of 240 = $ 0.10 \times 240 = 24 $
- 15% is halfway between 10% and 20%: $ \frac{24 + 48}{2} = 36 $

---

### **Final Answer**
$$
\boxed{36}
$$

Note: The reasoning parser captures the model’s step-by-step thinking process, allowing you to see how the model arrives at its conclusions.

4.2.3 Tool Calling

Step3-VL-10B supports tool calling capabilities. Enable the tool call parser:

Command

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --reasoning-parser deepseek-r1 \
  --tool-call-parser hermes \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

Python Example (with Thinking Process):

Example

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Define available tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Make request with streaming to see thinking process
response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    temperature=0.7,
    stream=True,
    extra_body={"top_k": -1}
)

# Process streaming response
thinking_started = False
has_thinking = False
tool_calls_accumulator = {}

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Accumulate tool calls
        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            # Close thinking section if needed
            if has_thinking and thinking_started:
                print("\n=============== Content =================\n", flush=True)
                thinking_started = False

            for tool_call in delta.tool_calls:
                index = tool_call.index
                if index not in tool_calls_accumulator:
                    tool_calls_accumulator[index] = {
                        'name': None,
                        'arguments': ''
                    }

                if tool_call.function:
                    if tool_call.function.name:
                        tool_calls_accumulator[index]['name'] = tool_call.function.name
                    if tool_call.function.arguments:
                        tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments

        # Print content
        if delta.content:
            print(delta.content, end="", flush=True)

# Print accumulated tool calls
for index, tool_call in sorted(tool_calls_accumulator.items()):
    print(f"Tool Call: {tool_call['name']}")
    print(f"   Arguments: {tool_call['arguments']}")

print()

Example Output:

Output

=============== Thinking =================
The user is asking about the weather in Beijing. I have a function called "get_weather" that can provide weather information for a location. Let me check the parameters:

- location: required (string) - "Beijing"
- unit: optional (string, enum: ["celsius", "fahrenheit"]) - not specified by the user, so I won't include it

I should call the function with location="Beijing".

<tool_calls>

=============== Content =================

</tool_calls>Tool Call: get_weather
   Arguments: {"location": "Beijing"}

Handling Tool Call Results:

Example

# After getting the tool call, execute the function
def get_weather(location, unit="celsius"):
    # Your actual weather API call here
    return f"The weather in {location} is 22°{unit[0].upper()} and sunny."

# Send tool result back to the model
messages = [
    {"role": "user", "content": "What's the weather in Beijing?"},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [{
            "id": "call_123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": '{"location": "Beijing", "unit": "celsius"}'
            }
        }]
    },
    {
        "role": "tool",
        "tool_call_id": "call_123",
        "content": get_weather("Beijing", "celsius")
    }
]

final_response = client.chat.completions.create(
    model="stepfun-ai/Step3-VL-10B",
    messages=messages,
    temperature=0.7,
    extra_body={"top_k": -1}
)

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

Note:

The reasoning parser shows how the model decides to use a tool
Tool calls are clearly marked with the function name and arguments
You can then execute the function and send the result back to continue the conversation

5. Benchmark

5.1 Speed Benchmark

Test Environment:

Hardware: NVIDIA B200 GPU (1x)
Model: stepfun-ai/Step3-VL-10B
Tensor Parallelism: 1
sglang version: 0.5.8+

We use SGLang’s built-in benchmarking tool to conduct performance evaluation with random images.

5.1.1 Latency-Sensitive Benchmark

Model Deployment Command:

Command

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

Benchmark Command:

Command

python3 -m sglang.bench_serving \
  --backend sglang-oai-chat \
  --host 127.0.0.1 \
  --port 30000 \
  --model stepfun-ai/Step3-VL-10B \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 \
  --random-output-len 1024 \
  --num-prompts 10 \
  --max-concurrency 1

Result:

Output

============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  30.85
Total input tokens:                      14120
Total input text tokens:                 720
Total input vision tokens:               13400
Total generated tokens:                  4220
Total generated tokens (retokenized):    4217
Request throughput (req/s):              0.32
Input token throughput (tok/s):          457.71
Output token throughput (tok/s):         136.79
Peak output token throughput (tok/s):    240.00
Peak concurrent requests:                2
Total token throughput (tok/s):          594.50
Concurrency:                             1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   3083.40
Median E2E Latency (ms):                 2747.00
P90 E2E Latency (ms):                    4574.50
P99 E2E Latency (ms):                    5462.49
---------------Time to First Token----------------
Mean TTFT (ms):                          1327.69
Median TTFT (ms):                        1341.01
P99 TTFT (ms):                           1486.11
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          4.16
Median TPOT (ms):                        4.17
P99 TPOT (ms):                           4.18
---------------Inter-Token Latency----------------
Mean ITL (ms):                           4.17
Median ITL (ms):                         4.18
P95 ITL (ms):                            4.30
P99 ITL (ms):                            4.38
Max ITL (ms):                            8.24
==================================================

5.1.2 Throughput-Sensitive Benchmark

Benchmark Command:

Command

python3 -m sglang.bench_serving \
  --backend sglang-oai-chat \
  --host 127.0.0.1 \
  --port 30000 \
  --model stepfun-ai/Step3-VL-10B \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 \
  --random-output-len 1024 \
  --num-prompts 1000 \
  --max-concurrency 100

Result:

Output

============ Serving Benchmark Result ============
Backend:                                 sglang-oai-chat
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  976.52
Total input tokens:                      1416949
Total input text tokens:                 76949
Total input vision tokens:               1340000
Total generated tokens:                  510855
Total generated tokens (retokenized):    510526
Request throughput (req/s):              1.02
Input token throughput (tok/s):          1451.02
Output token throughput (tok/s):         523.14
Peak output token throughput (tok/s):    20429.00
Peak concurrent requests:                103
Total token throughput (tok/s):          1974.16
Concurrency:                             99.81
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   97463.22
Median E2E Latency (ms):                 91872.75
P90 E2E Latency (ms):                    118553.42
P99 E2E Latency (ms):                    198445.56
---------------Time to First Token----------------
Mean TTFT (ms):                          94379.07
Median TTFT (ms):                        87163.09
P99 TTFT (ms):                           194871.41
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          5.89
Median TPOT (ms):                        5.72
P99 TPOT (ms):                           23.58
---------------Inter-Token Latency----------------
Mean ITL (ms):                           6.05
Median ITL (ms):                         0.13
P95 ITL (ms):                            0.56
P99 ITL (ms):                            3.99
Max ITL (ms):                            97551.06
==================================================

5.2 Accuracy Benchmark

5.2.1 MMMU Benchmark

You can evaluate the model’s accuracy using the MMMU dataset:

Model Deployment Command:

Command

python -m sglang.launch_server \
  --model stepfun-ai/Step3-VL-10B \
  --host 0.0.0.0 \
  --port 30000 \
  --trust-remote-code

Benchmark Command:

Command

python3 benchmark/mmmu/bench_sglang.py \
    --port 30000 \
    --concurrency 64

Result:

Output

Benchmark time: 934.6179109360091
answers saved to: ./answer_sglang.json
Evaluating...
answers saved to: ./answer_sglang.json
{'Accounting': {'acc': 0.667, 'num': 30},
 'Agriculture': {'acc': 0.367, 'num': 30},
 'Architecture_and_Engineering': {'acc': 0.4, 'num': 30},
 'Art': {'acc': 0.467, 'num': 30},
 'Art_Theory': {'acc': 0.5, 'num': 30},
 'Basic_Medical_Science': {'acc': 0.367, 'num': 30},
 'Biology': {'acc': 0.3, 'num': 30},
 'Chemistry': {'acc': 0.467, 'num': 30},
 'Clinical_Medicine': {'acc': 0.567, 'num': 30},
 'Computer_Science': {'acc': 0.467, 'num': 30},
 'Design': {'acc': 0.567, 'num': 30},
 'Diagnostics_and_Laboratory_Medicine': {'acc': 0.3, 'num': 30},
 'Economics': {'acc': 0.6, 'num': 30},
 'Electronics': {'acc': 0.567, 'num': 30},
 'Energy_and_Power': {'acc': 0.633, 'num': 30},
 'Finance': {'acc': 0.733, 'num': 30},
 'Geography': {'acc': 0.333, 'num': 30},
 'History': {'acc': 0.533, 'num': 30},
 'Literature': {'acc': 0.533, 'num': 30},
 'Manage': {'acc': 0.6, 'num': 30},
 'Marketing': {'acc': 0.767, 'num': 30},
 'Materials': {'acc': 0.6, 'num': 30},
 'Math': {'acc': 0.7, 'num': 30},
 'Mechanical_Engineering': {'acc': 0.333, 'num': 30},
 'Music': {'acc': 0.4, 'num': 30},
 'Overall': {'acc': 0.523, 'num': 900},
 'Overall-Art and Design': {'acc': 0.483, 'num': 120},
 'Overall-Business': {'acc': 0.673, 'num': 150},
 'Overall-Health and Medicine': {'acc': 0.513, 'num': 150},
 'Overall-Humanities and Social Science': {'acc': 0.492, 'num': 120},
 'Overall-Science': {'acc': 0.5, 'num': 150},
 'Overall-Tech and Engineering': {'acc': 0.481, 'num': 210},
 'Pharmacy': {'acc': 0.6, 'num': 30},
 'Physics': {'acc': 0.7, 'num': 30},
 'Psychology': {'acc': 0.467, 'num': 30},
 'Public_Health': {'acc': 0.733, 'num': 30},
 'Sociology': {'acc': 0.433, 'num': 30}}
eval out saved to ./val_sglang.json
Overall accuracy: 0.523

Cookbook

​1. Model Introduction

​2. SGLang Installation

​3. Model Deployment

​3.1 Basic Configuration

​3.2 Configuration Tips

​4. Model Invocation

​4.1 Basic Usage

​4.2 Advanced Usage

​4.2.1 Multi-Modal Inputs

​4.2.2 Reasoning Parser

​4.2.3 Tool Calling

​5. Benchmark

​5.1 Speed Benchmark

​5.1.1 Latency-Sensitive Benchmark

​5.1.2 Throughput-Sensitive Benchmark

​5.2 Accuracy Benchmark

​5.2.1 MMMU Benchmark

1. Model Introduction

2. SGLang Installation

3. Model Deployment

3.1 Basic Configuration

3.2 Configuration Tips

4. Model Invocation

4.1 Basic Usage

4.2 Advanced Usage

4.2.1 Multi-Modal Inputs

4.2.2 Reasoning Parser

4.2.3 Tool Calling

5. Benchmark

5.1 Speed Benchmark

5.1.1 Latency-Sensitive Benchmark

5.1.2 Throughput-Sensitive Benchmark

5.2 Accuracy Benchmark

5.2.1 MMMU Benchmark