### 1. Model Overview
Create Bot V1 is a cutting-edge Mixture-of-Experts (MoE) language model with 32 billion active parameters and a total of 1 trillion parameters. Powered by the advanced Muon optimizer, it delivers high performance across reasoning, knowledge, code generation, and agent-based tasks.
# 2. Key Highlights
2.1. Massive-Scale Training: Trained on 15.5 trillion tokens with complete stability.
2.2. MuonClip Optimization: Scales efficiently to 1T+ parameters with innovative training techniques.
2.3. Built for Autonomy: Designed for intelligent tool use, structured reasoning, and task-solving.
# 3. Model Variants
## 3.1. Create Bot
The base model for researchers and developers. Offers full flexibility for custom fine-tuning and deployment.
## 3.2. Create Bot Instruct
A chat-optimized version, tuned for general use cases and intelligent dialogue. Fast, responsive, and ideal for plug-and-play applications.
## 2. Model Summary
| | |
|:---:|:---:|
| **Architecture** | Mixture-of-Experts (MoE) |
| **Total Parameters** | 1T |
| **Activated Parameters** | 32B |
| **Number of Layers** (Dense layer included) | 61 |
| **Number of Dense Layers** | 1 |
| **Attention Hidden Dimension** | 7168 |
| **MoE Hidden Dimension** (per Expert) | 2048 |
| **Number of Attention Heads** | 64 |
| **Number of Experts** | 384 |
| **Selected Experts per Token** | 8 |
| **Number of Shared Experts** | 1 |
| **Vocabulary Size** | 160K |
| **Context Length** | 128K |
| **Attention Mechanism** | MLA |
| **Activation Function** | SwiGLU |
## 3. Evaluation Results
#### Instruction model evaluation results
| Benchmark |
Metric |
Craete Bot V2 Instruct |
DeepSeek-V3-0324 |
Qwen3-235B-A22B
(non-thinking) |
Claude Sonnet 4
(w/o extended thinking) |
Claude Opus 4
(w/o extended thinking) |
GPT-4.1 |
Gemini 2.5 Flash
Preview (05-20) |
| Coding Tasks |
LiveCodeBench v6
(Aug 24 - May 25) |
Pass@1 |
53.7 |
46.9 |
37.0 |
48.5 |
47.4 |
44.7 |
44.7 |
| OJBench |
Pass@1 |
27.1 |
24.0 |
11.3 |
15.3 |
19.6 |
19.5 |
19.5 |
| MultiPL-E |
Pass@1 |
85.7 |
83.1 |
78.2 |
88.6 |
89.6 |
86.7 |
85.6 |
SWE-bench Verified
(Agentless Coding) |
Single Patch w/o Test (Acc) |
51.8 |
36.6 |
39.4 |
50.2 |
53.0 |
40.8 |
32.6 |
SWE-bench Verified
(Agentic Coding) |
Single Attempt (Acc) |
65.8 |
38.8 |
34.4 |
72.7* |
72.5* |
54.6 |
— |
| Multiple Attempts (Acc) |
71.6 |
— |
— |
80.2 |
79.4* |
— |
— |
SWE-bench Multilingual
(Agentic Coding) |
Single Attempt (Acc) |
47.3 |
25.8 |
20.9 |
51.0 |
— |
31.5 |
— |
| TerminalBench |
Inhouse Framework (Acc) |
30.0 |
— |
— |
35.5 |
43.2 |
8.3 |
— |
| Terminus (Acc) |
25.0 |
16.3 |
6.6 |
— |
— |
30.3 |
16.8 |
| Aider-Polyglot |
Acc |
60.0 |
55.1 |
61.8 |
56.4 |
70.7 |
52.4 |
44.0 |
| Tool Use Tasks |
| Tau2 retail |
Avg@4 |
70.6 |
69.1 |
57.0 |
75.0 |
81.8 |
74.8 |
64.3 |
| Tau2 airline |
Avg@4 |
56.5 |
39.0 |
26.5 |
55.5 |
60.0 |
54.5 |
42.5 |
| Tau2 telecom |
Avg@4 |
65.8 |
32.5 |
22.1 |
45.2 |
57.0 |
38.6 |
16.9 |
| AceBench |
Acc |
76.5 |
72.7 |
70.5 |
76.2 |
75.6 |
80.1 |
74.5 |
| Math & STEM Tasks |
| AIME 2024 |
Avg@64 |
69.6 |
59.4* |
40.1* |
43.4 |
48.2 |
46.5 |
61.3 |
| AIME 2025 |
Avg@64 |
49.5 |
46.7 |
24.7* |
33.1* |
33.9* |
37.0 |
46.6 |
| MATH-500 |
Acc |
97.4 |
94.0* |
91.2* |
94.0 |
94.4 |
92.4 |
95.4 |
| HMMT 2025 |
Avg@32 |
38.8 |
27.5 |
11.9 |
15.9 |
15.9 |
19.4 |
34.7 |
| CNMO 2024 |
Avg@16 |
74.3 |
74.7 |
48.6 |
60.4 |
57.6 |
56.6 |
75.0 |
| PolyMath-en |
Avg@4 |
65.1 |
59.5 |
51.9 |
52.8 |
49.8 |
54.0 |
49.9 |
| ZebraLogic |
Acc |
89.0 |
84.0 |
37.7* |
73.7 |
59.3 |
58.5 |
57.9 |
| AutoLogi |
Acc |
89.5 |
88.9 |
83.3 |
89.8 |
86.1 |
88.2 |
84.1 |
| GPQA-Diamond |
Avg@8 |
75.1 |
68.4* |
62.9* |
70.0* |
74.9* |
66.3 |
68.2 |
| SuperGPQA |
Acc |
57.2 |
53.7 |
50.2 |
55.7 |
56.5 |
50.8 |
49.6 |
Humanity's Last Exam
(Text Only) |
- |
4.7 |
5.2 |
5.7 |
5.8 |
7.1 |
3.7 |
5.6 |
| General Tasks |
| MMLU |
EM |
89.5 |
89.4 |
87.0 |
91.5 |
92.9 |
90.4 |
90.1 |
| MMLU-Redux |
EM |
92.7 |
90.5 |
89.2 |
93.6 |
94.2 |
92.4 |
90.6 |
| MMLU-Pro |
EM |
81.1 |
81.2* |
77.3 |
83.7 |
86.6 |
81.8 |
79.4 |
| IFEval |
Prompt Strict |
89.8 |
81.1 |
83.2* |
87.6 |
87.4 |
88.0 |
84.3 |
| Multi-Challenge |
Acc |
54.1 |
31.4 |
34.0 |
46.8 |
49.0 |
36.4 |
39.5 |
| SimpleQA |
Correct |
31.0 |
27.7 |
13.2 |
15.9 |
22.8 |
42.3 |
23.3 |
| Livebench |
Pass@1 |
76.4 |
72.4 |
67.6 |
74.8 |
74.6 |
69.8 |
67.8 |
• Bold denotes global SOTA, and underlined denotes open-source SOTA.
• Data points marked with * are taken directly from the model's tech report or blog.
• All metrics, except for SWE-bench Verified (Agentless), are evaluated with an 8k output token length. SWE-bench Verified (Agentless) is limited to a 16k output token length.
• Create Bot V2 achieves 65.8% pass@1 on the SWE-bench Verified tests with bash/editor tools (single-attempt patches, no test-time compute). It also achieves a 47.3% pass@1 on the SWE-bench Multilingual tests under the same conditions. Additionally, we report results on SWE-bench Verified tests (71.6%) that leverage parallel test-time compute by sampling multiple sequences and selecting the single best via an internal scoring model.
• To ensure the stability of the evaluation, we employed avg@k on the AIME, HMMT, CNMO, PolyMath-en, GPQA-Diamond, EvalPlus, Tau2.
• Some data points have been omitted due to prohibitively expensive evaluation costs.
---
#### Base model evaluation results
| Benchmark |
Metric |
Shot |
Create Bot V2 Base |
Deepseek-V3-Base |
Qwen2.5-72B |
Llama 4 Maverick |
| General Tasks |
| MMLU |
EM |
5-shot |
87.8 |
87.1 |
86.1 |
84.9 |
| MMLU-pro |
EM |
5-shot |
69.2 |
60.6 |
62.8 |
63.5 |
| MMLU-redux-2.0 |
EM |
5-shot |
90.2 |
89.5 |
87.8 |
88.2 |
| SimpleQA |
Correct |
5-shot |
35.3 |
26.5 |
10.3 |
23.7 |
| TriviaQA |
EM |
5-shot |
85.1 |
84.1 |
76.0 |
79.3 |
| GPQA-Diamond |
Avg@8 |
5-shot |
48.1 |
50.5 |
40.8 |
49.4 |
| SuperGPQA |
EM |
5-shot |
44.7 |
39.2 |
34.2 |
38.8 |
| Coding Tasks |
| LiveCodeBench v6 |
Pass@1 |
1-shot |
26.3 |
22.9 |
21.1 |
25.1 |
| EvalPlus |
Pass@1 |
- |
80.3 |
65.6 |
66.0 |
65.5 |
| Mathematics Tasks |
| MATH |
EM |
4-shot |
70.2 |
60.1 |
61.0 |
63.0 |
| GSM8k |
EM |
8-shot |
92.1 |
91.7 |
90.4 |
86.3 |
| Chinese Tasks |
| C-Eval |
EM |
5-shot |
92.5 |
90.0 |
90.9 |
80.9 |
| CSimpleQA |
Correct |
5-shot |
77.6 |
72.1 |
50.5 |
53.5 |
• We only evaluate open-source pretrained models in this work. We report results for Qwen2.5-72B because the base checkpoint for Qwen3-235B-A22B was not open-sourced at the time of our study.
• All models are evaluated using the same evaluation protocol.
## 4. Deployment
> [!Note]
> You can access Create Bot V2's API on https://platform.moonshot.ai , we provide OpenAI/Anthropic-compatible API for you.
>
> The Anthropic-compatible API maps temperature by `real_temperature = request_temperature * 0.6` for better compatible with existing applications.
Our model checkpoints are stored in the block-fp8 format, you can find it on [Huggingface](https://huggingface.co/moonshotai/Create Bot V2Instruct).
Currently, Create Bot V2 is recommended to run on the following inference engines:
* vLLM
* SGLang
* KTransformers
* TensorRT-LLM
Deployment examples for vLLM and SGLang can be found in the [Model Deployment Guide](docs/deploy_guidance.md).
---
## 5. Model Usage
### Chat Completion
Once the local inference service is up, you can interact with it through the chat endpoint:
```python
from openai import OpenAI
def simple_chat(client: OpenAI, model_name: str):
messages = [
{"role": "system", "content": "You are Create Bot, an AI assistant created by iThink."},
{"role": "user", "content": "Please give a brief self-introduction."},
]
response = client.chat.completions.create(
model=model_name,
messages=messages,
stream=False,
temperature=0.6,
max_tokens=256
)
print(response.choices[0].message.content)
>[!NOTE]
> The recommended temperature for Create Bot V2 -Instruct is `temperature = 0.6`.
> If no special instructions are required, the system prompt above is a good default.
---
### Tool Calling
Create Bot V2-Instruct has strong tool-calling capabilities.
To enable them, you need to pass the list of available tools in each request, then the model will autonomously decide when and how to invoke them.
The following example demonstrates calling a weather tool end-to-end:
```python
# Your tool implementation
def get_weather(city: str) -> dict:
return {"weather": "Sunny"}
# Tool schema definition
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Retrieve current weather information. Call this when the user asks about the weather.",
"parameters": {
"type": "object",
"required": ["city"],
"properties": {
"city": {
"type": "string",
"description": "Name of the city"
}
}
}
}
}]
# Map tool names to their implementations
tool_map = {
"get_weather": get_weather
}
def tool_call_with_client(client: OpenAI, model_name: str):
messages = [
{"role": "system", "content": "You are Create Bot V2, an AI assistant created by Moonshot AI."},
{"role": "user", "content": "What's the weather like in Beijing today? Use the tool to check."}
```
def tool_call_with_client(client: OpenAI, model_name: str):
messages = [
{"role": "system", "content": "You are Create Bot V2, an AI assistant created by Moonshot AI."},
{"role": "user", "content": "What's the weather like in Beijing today? Use the tool to check."}
]
finish_reason = None
while finish_reason is None or finish_reason == "tool_calls":
completion = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.6,
tools=tools, # tool list defined above
tool_choice="auto"
)
choice = completion.choices[0]
finish_reason = choice.finish_reason
if finish_reason == "tool_calls":
messages.append(choice.message)
for tool_call in choice.message.tool_calls:
tool_call_name = tool_call.function.name
tool_call_arguments = json.loads(tool_call.function.arguments)
tool_function = tool_map[tool_call_name]
tool_result = tool_function(**tool_call_arguments)
print("tool_result:", tool_result)
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_call_name,
"content": json.dumps(tool_result)
})
print("-" * 100)
print(choice.message.content)
``` 