---
title: Agnes-2.5-Pro-Alpha
canonical_url: "https://www.modelscope.cn/models/Agnes-AI/Agnes-2.5-Pro-Alpha"
md_url: "https://www.modelscope.cn/models/Agnes-AI/Agnes-2.5-Pro-Alpha.md"
repository: Agnes-AI/Agnes-2.5-Pro-Alpha
last_updated: 2026-09-14
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
- image-text-to-text
model_type:
- qwen3_5_moe
architectures:
- Qwen3_5MoeForConditionalGeneration
base_model:
- Qwen/Qwen3.5-397B-A17B
base_model_relation: finetune
parameters: 396.8B
tensor_type:
- BF16
library_name:
- safetensors
- pytorch
frameworks:
- pytorch
language:
- en
- zh
downloads: 25
stars: 1
tags:
- agnes-ai
- reasoning
- multimodal
- long-context
- api
---
# Agnes-2.5-Pro-Alpha
> Agnes-2.5-Pro-Alpha - Agnes-AI 在 ModelScope 开源的模型。Hello! 👋 Today we are introducing Agnes 2.5 Pro Alpha, our most capable reasoning model for advanced coding, scientific problem solving, long-context analysis, multimodal understanding, and agentic workflows.
Agnes-AI/Agnes-2.5-Pro-Alpha 是 ModelScope 魔搭社区上的 396.8B 参数image-text-to-text模型，采用 apache-2.0 许可，基于 Qwen/Qwen3.5-397B-A17B 构建。
- **Repository**: Agnes-AI/Agnes-2.5-Pro-Alpha
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 396.8B
- **Base model**: Qwen/Qwen3.5-397B-A17B
- **Tags**: agnes-ai, reasoning, multimodal, long-context, api
- **Downloads**: 25
- **Stars**: 1
- **Last updated**: 2026-09-14
Source: https://www.modelscope.cn/models/Agnes-AI/Agnes-2.5-Pro-Alpha
---
# Agnes 2.5 Pro Alpha
Hello! 👋 Today we are introducing **Agnes 2.5 Pro Alpha**, our most capable reasoning model for advanced coding, scientific problem solving, long-context analysis, multimodal understanding, and agentic workflows.
Highlights:
- **Competitive benchmark performance:** Agnes outperforms Qwen3.5-397B on six of the eight evaluations visualized below, with particularly clear gains on Terminal-Bench v2.1, CritPt, and AA-Omniscience Accuracy.
- **Built for demanding work:** a **1M-token context window**, up to **65,536 output tokens**, extended reasoning, tool calling, and text, image understanding.
## Agnes 2.5 Pro Alpha
A multimodal reasoning model available through the Agnes AI API. The model combines long-context understanding with strong coding and scientific reasoning, while retaining the throughput and pricing needed for production workloads.
### Benchmarks
Agnes 2.5 Pro Alpha is evaluated against the same comparison set selected for Ornith-1.0-397B: Qwen3.5-397B, Qwen3.7-Max, GLM-5.2-744B, MiniMax-M3-428B, DeepSeek-V4-Pro-1.6T, Claude Opus 4.7, and Claude Opus 4.8. Every result below is an independent Artificial Analysis benchmark measurement.
| Benchmark |
Agnes 2.5
Pro Alpha |
Qwen3.5
397B |
Qwen3.7
Max |
GLM-5.2
744B |
MiniMax-M3
428B |
DeepSeek-V4-Pro
1.6T |
Claude Opus
4.7 |
Claude Opus
4.8 |
| Agentic Work & Coding |
| GDPval-AA v2 † | 33.8 | 23.2 | 38.6 | 50.3 | 44.3 | 54.5 | 49.5 | 54.2 |
| τ³-Banking | 12.4 | 13.4 | 11.8 | 34.6 | 15.3 | 39.6 | 34.6 | 34.2 |
| Terminal-Bench v2.1 | 67.0 | 51.3 | 74.5 | 77.9 | 65.2 | 78.7 | 83.1 | 84.6 |
| SciCode | 42.2 | 42.0 | 48.8 | 50.5 | 45.4 | 49.2 | 54.5 | 53.5 |
| Long Context & Scientific Reasoning |
| AA-LCR | 73.0 | 72.7 | 74.7 | 76.7 | 80.3 | 75.3 | 75.3 | 73.0 |
| Humanity's Last Exam | 33.6 | 29.0 | 40.5 | 41.1 | 39.0 | 41.0 | 42.3 | 48.7 |
| GPQA Diamond | 87.6 | 89.3 | 92.3 | 89.5 | 92.9 | 92.8 | 91.4 | 92.0 |
| CritPt | 10.9 | 1.7 | 13.4 | 20.9 | 3.7 | 18.0 | 12.0 | 20.9 |
| Knowledge Reliability |
| AA-Omniscience Accuracy | 33.5 | 30.8 | 31.1 | 24.3 | 16.7 | 49.1 | 48.9 | 48.8 |
| Non-Hallucination Rate | 11.9 | 11.1 | 74.4 | 73.7 | 81.6 | 5.2 | 57.7 | 60.7 |
† GDPval-AA v2 uses Artificial Analysis' normalized score, (Elo − 500) / 2000. Non-Hallucination Rate is 1 − hallucination rate. Higher is better for every benchmark. Snapshot checked August 18, 2026; values may change as evaluations are updated.
## Model Information
| Property | Value |
|---|---|
| Developed by | Agnes AI |
| Model name | Agnes 2.5 Pro Alpha |
| Model type | Multimodal reasoning model |
| License | Apache License 2.0 |
| Languages | English, Chinese |
| Context window | 1,048,576 tokens |
| Maximum output | 65,536 tokens |
| Input modalities | Text, image |
| Output modality | Text |
| Precision | BF16 |
| Tool calling | Yes |
| Streaming | Yes |
| Release date | July 2026 |
## License
This repository is licensed under the [Apache License 2.0](LICENSE).
Agnes 2.5 Pro Alpha is a post-trained derivative of [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B), which is also licensed under Apache License 2.0. Original copyright notices are retained. Additional post-training was performed by Agnes AI.
See the `LICENSE` file in this repository for the full terms.
## Hardware Requirements
The Quickstart launch command uses 8-GPU tensor parallelism. The checkpoint is a large multi-shard BF16 package; a single GPU is not sufficient.
| Resource | Recommendation |
|---|---|
| GPUs | 8× NVIDIA H200 (141 GB) or equivalent |
| Tensor parallel | `--tp 8` |
| Host memory / disk | Fast NVMe with about **1 TB** free for weights, tokenizer files, and download cache |
| Context length | The sample command sets `--context-length 1024000`. If you hit out-of-memory errors, lower this value |
| Network | Optional. The same model is also served at `https://apihub.agnes-ai.com/v1` without local GPUs |
## Quickstart
REASONING MODEL
Agnes 2.5 Pro Alpha uses extended reasoning for complex tasks and is available through OpenAI-compatible Chat Completions and Responses APIs. Keep API keys in environment variables and use publicly accessible URLs for image inputs.
SGLang
```bash
python -m sglang.launch_server \
--model-path Agnes-AI/Agnes-2.5-Pro-Alpha \
--served-model-name agnes-2.5-pro-alpha \
--tp 8 \
--host 0.0.0.0 --port 8000 \
--context-length 1024000 \
--mem-fraction-static 0.85 \
--tool-call-parser qwen3_coder \
--reasoning-parser qwen3
```
### Chat Completions
```bash
export AGNES_API_KEY="your-api-key"
curl https://apihub.agnes-ai.com/v1/chat/completions \
-H "Authorization: Bearer ${AGNES_API_KEY}" \
-H "Content-Type: application/json" \
-d '{
"model": "agnes-2.5-pro-alpha",
"messages": [
{
"role": "user",
"content": "Review this API handler for security issues and provide a corrected version."
}
],
"temperature": 1.0,
"max_tokens": 2000
}'
```
### Python
```bash
pip install openai
```
```python
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AGNES_API_KEY"],
base_url="https://apihub.agnes-ai.com/v1",
)
response = client.chat.completions.create(
model="agnes-2.5-pro-alpha",
messages=[
{
"role": "user",
"content": "Design a fault-tolerant event processing architecture.",
}
],
temperature=1.0,
max_tokens=2000,
)
print(response.choices[0].message.content)
```
### Image Understanding
```python
response = client.chat.completions.create(
model="agnes-2.5-pro-alpha",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Explain this chart and call out anomalies."},
{
"type": "image_url",
"image_url": {"url": "https://example.com/chart.png"},
},
],
}
],
)
```
### Responses API
```bash
curl https://apihub.agnes-ai.com/v1/responses \
-H "Authorization: Bearer ${AGNES_API_KEY}" \
-H "Content-Type: application/json" \
-d '{
"model": "agnes-2.5-pro-alpha",
"input": "Create a step-by-step migration plan from a monolith to services.",
"max_output_tokens": 2000
}'
```
## Recommended Inference Settings
Use sampling rather than greedy decoding. Leave enough `max_tokens` / `max_output_tokens` for extended reasoning.
| Setting | Recommended |
|---|---|
| `temperature` | 1.0 |
| `top_p` | 0.95 |
| `top_k` | 20 |
| `repetition_penalty` | 1.05 |
| `max_tokens` | 2000 or higher |
Raise `max_tokens` if a response stops early.
## Model Capabilities
| Capability | Support |
|---|---|
| Advanced reasoning | Yes |
| Coding and debugging | Yes |
| Long-context analysis | 1M tokens |
| Maximum output | 65,536 tokens |
| Image understanding | Yes, via public image URL |
| Tool calling | Yes |
| Streaming | Yes |
| OpenAI-compatible APIs | Chat Completions and Responses |
Agnes 2.5 Pro Alpha is especially well suited to repository-level coding, technical research, document synthesis, visual analysis, and tool-enabled agents that need to reason across long and complex contexts.
## Responsible Use
Model outputs can contain errors. Validate high-impact decisions and tool actions in the application layer, and review the applicable Agnes AI service terms before sending sensitive or regulated data.
## Citation
```bibtex
@misc{agnes25proalpha2026,
title = {Agnes 2.5 Pro Alpha},
author = {{Agnes AI}},
year = {2026},
month = jul,
howpublished = {API model},
url = {https://wiki.agnes-ai.com/en/docs/agnes-25-pro-alpha}
}
```