mirror of
https://github.com/hanzo-docs/docs.git
synced 2026-10-29 07:01:44 +00:00
zeekay
4bd5378921
- packages/zen-models: rename package to @zenlm/models, update all source comments - zen-docs: fix "Mixture of Distilled Experts" → "Mixture of Diverse Experts" across all blog posts and docs content (models.mdx, index.mdx, zen-mode-architecture.mdx, zen4-ultra.mdx, zen5-release.mdx, drop-upcycling-zen-mode.mdx) - apps/docs: fix brand copy on home page
168 lines
4.9 KiB
Plaintext
168 lines
4.9 KiB
Plaintext
---
|
||
title: "Zen4 Ultra: 480B Parameters, 1M Token Context"
|
||
description: "Zen4 Ultra is our most capable model: 480B total parameters, 35B active per token, 1M token context window. Benchmark results and use cases."
|
||
date: "2026-01-20"
|
||
author: "Zen LM Team"
|
||
tags: ["Models", "Zen4", "Flagship"]
|
||
---
|
||
|
||
[GITHUB](https://github.com/hanzoai) [HUGGING FACE](https://huggingface.co/hanzoai/zen4-ultra) [TRY ZEN CHAT](https://hanzo.ai/chat)
|
||
|
||
**Zen4 Ultra** is the most capable model in the Zen4 family. It is a Mixture of Diverse Experts (MoE) model with 480B total parameters and 35B active parameters per forward pass. The native context window is 256K tokens, extending to 1M tokens with YaRN extrapolation.
|
||
|
||
## Architecture
|
||
|
||
Property| Value
|
||
---|---
|
||
Total parameters| 480B
|
||
Active parameters per token| 35B
|
||
Experts per layer| 128
|
||
Top-k routing| 8
|
||
Context window (native)| 256K
|
||
Context window (YaRN)| 1M
|
||
Vocabulary size| 151,936
|
||
Attention heads| 64
|
||
KV heads (GQA)| 8
|
||
Layers| 94
|
||
|
||
## Benchmark Results
|
||
|
||
### General Reasoning
|
||
|
||
Benchmark| Zen4 Ultra| Zen Max 72B
|
||
---|---|---
|
||
MMLU| 89.4| 87.1
|
||
MMLU-Pro| 75.2| 71.8
|
||
ARC-Challenge| 72.1| 68.4
|
||
HellaSwag| 92.3| 90.1
|
||
Winogrande| 87.6| 85.2
|
||
|
||
### Mathematics
|
||
|
||
Benchmark| Zen4 Ultra| Zen Max 72B
|
||
---|---|---
|
||
MATH| 81.4| 73.2
|
||
GSM8K| 95.3| 92.1
|
||
AMC 2023| 62.4| 54.7
|
||
AIME 2024| 48.2| 37.6
|
||
|
||
### Code
|
||
|
||
Benchmark| Zen4 Ultra| Zen Max 72B
|
||
---|---|---
|
||
HumanEval| 91.2| 82.4
|
||
MBPP| 87.6| 81.3
|
||
LiveCodeBench| 52.4| 44.1
|
||
SWE-bench Verified| 45.7| 38.2
|
||
|
||
### Long Context
|
||
|
||
Task| Score at 32K| Score at 128K| Score at 512K
|
||
---|---|---|---
|
||
NIAH recall| 99.1%| 98.4%| 94.7%
|
||
Summarization| 48.2| 46.9| 43.1
|
||
QA over long doc| 74.3| 71.2| 64.8
|
||
|
||
Long-context performance remains strong through 512K tokens, with graceful degradation thereafter.
|
||
|
||
### Multilingual
|
||
|
||
Evaluated on 30 languages across MMMLU:
|
||
|
||
Language Group| Score
|
||
---|---
|
||
Latin script (high-resource)| 86.4
|
||
Latin script (low-resource)| 72.1
|
||
CJK| 81.3
|
||
Arabic/Hebrew| 76.8
|
||
Other non-Latin| 68.2
|
||
|
||
## Use Cases
|
||
|
||
### Complex Research and Analysis
|
||
|
||
Zen4 Ultra excels at tasks requiring synthesis across long documents:
|
||
|
||
* Analyzing regulatory filings spanning hundreds of pages
|
||
* Cross-referencing scientific literature for systematic reviews
|
||
* Multi-document legal analysis with citation tracking
|
||
* Financial model analysis with full spreadsheet context
|
||
|
||
The 1M token context allows loading entire codebases, large document sets, or extended conversation histories without truncation.
|
||
|
||
### Multi-Step Reasoning
|
||
|
||
For problems requiring planning and backtracking — competitive math, logic puzzles, complex software architecture decisions — Ultra’s depth provides measurable advantage over smaller models.
|
||
|
||
### Agentic Workflows
|
||
|
||
Ultra’s function calling reliability is critical for long-running agent tasks:
|
||
|
||
* SWE-bench Verified: 45.7% (full-repo software engineering tasks)
|
||
* Tool selection accuracy: 94.2% on held-out tool-use evaluation
|
||
* Multi-turn instruction adherence: 91.8%
|
||
|
||
### Code Generation
|
||
|
||
Near-human performance on competitive programming tasks. Generates complete, working implementations of complex algorithms in all major languages.
|
||
|
||
## Running Zen4 Ultra
|
||
|
||
### Hugging Face + vLLM (recommended for production)
|
||
```python
|
||
|
||
from vllm import LLM, SamplingParams
|
||
|
||
llm = LLM(
|
||
model="hanzoai/zen4-ultra",
|
||
tensor_parallel_size=8, # 8x H100 80GB
|
||
max_model_len=131072,
|
||
)
|
||
|
||
outputs = llm.generate(
|
||
["Explain the Zen MoDE architecture in detail."],
|
||
SamplingParams(temperature=0.7, max_tokens=2048),
|
||
)
|
||
|
||
|
||
```
|
||
|
||
### Transformers
|
||
```python
|
||
|
||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
import torch
|
||
|
||
model = AutoModelForCausalLM.from_pretrained(
|
||
"hanzoai/zen4-ultra",
|
||
torch_dtype=torch.bfloat16,
|
||
device_map="auto",
|
||
)
|
||
tokenizer = AutoTokenizer.from_pretrained("hanzoai/zen4-ultra")
|
||
|
||
messages = [{"role": "user", "content": "Solve: find all integer solutions to x^3 + y^3 = z^3."}]
|
||
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||
inputs = tokenizer(text, return_tensors="pt").to(model.device)
|
||
output = model.generate(**inputs, max_new_tokens=1024)
|
||
print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
|
||
|
||
|
||
```
|
||
|
||
### Hardware Requirements
|
||
|
||
Configuration| VRAM| Throughput
|
||
---|---|---
|
||
8x H100 80GB| 640GB| ~2,400 tok/s
|
||
16x A100 80GB| 1280GB| ~1,100 tok/s
|
||
32x A100 40GB| 1280GB| ~600 tok/s
|
||
|
||
For cost-sensitive production use cases, [Zen Max 72B](/blog/zen-max/) delivers most of Ultra’s capability at a fraction of the compute.
|
||
|
||
## License
|
||
|
||
Apache-2.0. Commercial use permitted. No royalty or usage fees.
|
||
|
||
* * *
|
||
|
||
_Zen4 Ultra is available now on[Hugging Face](https://huggingface.co/hanzoai/zen4-ultra). For API access, see [hanzo.ai](https://hanzo.ai)._ |