Models
zen-max
Trillion-parameter 1.04T MoE open-weights frontier model. Same model as zen4-max.
zen-max
Frontier
The open-weights release of the Zen frontier model. zen-max and zen4-max are the same model -- a 1.04T Mixture-of-Experts architecture with 32B active parameters per token and a 256K context window. zen-max is the brand name; zen4-max is the generation identifier.
Specifications
| Property | Value |
|---|---|
| Model ID | zen-max |
| Parameters | 1.04T (32B active) |
| Architecture | MoE |
| Context Window | 256K tokens |
| Status | Available |
| HuggingFace | zenlm/zen-max |
Capabilities
- Frontier-scale reasoning and analysis
- 256K context for entire codebases and document collections
- Complex multi-step problem solving
- Advanced multilingual understanding (100+ languages)
- State-of-the-art instruction following
- Research-grade text generation
Usage
HuggingFace
pip install transformers torch acceleratefrom transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-max")
model = AutoModelForCausalLM.from_pretrained(
"zenlm/zen-max",
device_map="auto",
torch_dtype="auto",
)
inputs = tokenizer("Solve this step by step:", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))API
from hanzoai import Hanzo
client = Hanzo(api_key="hk-your-api-key")
response = client.chat.completions.create(
model="zen-max",
messages=[{"role": "user", "content": "Derive the proof for the Cauchy-Schwarz inequality."}],
)
print(response.choices[0].message.content)See Also
- zen4-max -- Same model, generation identifier
- zen4-ultra -- zen4 with chain-of-thought reasoning
- zen4 -- 744B MoE flagship