---
title: Step3-VL-10B
metatags:
description: "Deploy Step3-VL-10B multimodal model with SGLang - compact 10B dense model with frontier-level vision understanding, complex reasoning, and tool calling capabilities."
---
import { Step3VL10BDeployment } from '/src/snippets/autoregressive/step-3vl-10b-deployment.jsx';
## 1. Model Introduction
[Step3-VL-10B](https://huggingface.co/stepfun-ai/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](https://huggingface.co/stepfun-ai/Step3-VL-10B).
## 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](../../../docs/get-started/install) 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:
- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
- [SGLang OpenAI Vision API Guide](../../../docs/basic_usage/openai_api_vision)
### 4.2 Advanced Usage
#### 4.2.1 Multi-Modal Inputs
Step3-VL-10B supports image inputs. Here's a basic example with image input:
```python 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:**
```text 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:
```python 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:**
```text 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:
```shell 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:**
```python 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:**
```text 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:
```shell 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):**
```python 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:**
```text 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".
=============== Content =================
Tool Call: get_weather
Arguments: {"location": "Beijing"}
```
**Handling Tool Call Results:**
```python 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:
```shell Command
python -m sglang.launch_server \
--model stepfun-ai/Step3-VL-10B \
--host 0.0.0.0 \
--port 30000 \
--trust-remote-code
```
- Benchmark Command:
```shell 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:
```text 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:
```shell 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:
```text 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:
```shell Command
python -m sglang.launch_server \
--model stepfun-ai/Step3-VL-10B \
--host 0.0.0.0 \
--port 30000 \
--trust-remote-code
```
- Benchmark Command:
```shell Command
python3 benchmark/mmmu/bench_sglang.py \
--port 30000 \
--concurrency 64
```
- Result:
```text 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
```