Agnes-2.5-Pro-Alpha

<p align="center"> <img width="132" src="assets/agnes_logo.svg" alt="Agnes AI logo"> </p> <p align="center"> <a href="https://agnes-ai.com/"><img src="https://img.shields.io/badge/Agnes_AI-Website-3248AF" alt="Agnes AI website"></a> <a href="https://wiki.agnes-ai.com/en/docs/agnes-25-pro-alpha"><img src="https://img.shields.io/badge/Agnes_2.5_Pro_Alpha-API_Docs-3248AF" alt="Agnes API docs"></a> <a href="https://artificialanalysis.ai/models/agnes-2-5-pro-alpha"><img src="https://img.shields.io/badge/Artificial_Analysis-Model_Page-111827" alt="Artificial Analysis model page"></a> </p>

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.

<img style="width:100%;max-width:1100px" src="assets/agnes_benchmarks.svg" alt="LLM benchmark evaluation comparing Agnes 2.5 Pro Alpha with flagship-scale models" title="Agnes 2.5 Pro Alpha benchmark evaluation">

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.

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;margin:0 auto;padding:16px 0;overflow-x:auto"> <table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:11px;min-width:1100px"> <thead><tr> <th style="width:20%;padding:10px 6px;text-align:left;border-bottom:2px solid #3248AF;color:#3248AF">Benchmark</th> <th style="width:10%;padding:10px 6px;text-align:center;font-weight:700;border-bottom:2px solid #3248AF;color:#3248AF;background:rgba(50,72,175,.09)">Agnes 2.5<br>Pro Alpha</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">Qwen3.5<br>397B</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">Qwen3.7<br>Max</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">GLM-5.2<br>744B</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">MiniMax-M3<br>428B</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">DeepSeek-V4-Pro<br>1.6T</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">Claude Opus<br>4.7</th> <th style="width:10%;padding:10px 6px;text-align:center;border-bottom:2px solid #3248AF">Claude Opus<br>4.8</th> </tr></thead> <tbody> <tr><td colspan="9" style="padding:8px 12px;font-weight:600;color:#3248AF;background:rgba(50,72,175,.07)">Agentic Work &amp; Coding</td></tr> <tr><td style="padding:7px">GDPval-AA v2 ^†</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">33.8</td><td style="padding:7px;text-align:center">23.2</td><td style="padding:7px;text-align:center">38.6</td><td style="padding:7px;text-align:center">50.3</td><td style="padding:7px;text-align:center">44.3</td><td style="padding:7px;text-align:center">54.5</td><td style="padding:7px;text-align:center">49.5</td><td style="padding:7px;text-align:center">54.2</td></tr> <tr><td style="padding:7px">τ³-Banking</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">12.4</td><td style="padding:7px;text-align:center">13.4</td><td style="padding:7px;text-align:center">11.8</td><td style="padding:7px;text-align:center">34.6</td><td style="padding:7px;text-align:center">15.3</td><td style="padding:7px;text-align:center">39.6</td><td style="padding:7px;text-align:center">34.6</td><td style="padding:7px;text-align:center">34.2</td></tr> <tr><td style="padding:7px">Terminal-Bench v2.1</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">67.0</td><td style="padding:7px;text-align:center">51.3</td><td style="padding:7px;text-align:center">74.5</td><td style="padding:7px;text-align:center">77.9</td><td style="padding:7px;text-align:center">65.2</td><td style="padding:7px;text-align:center">78.7</td><td style="padding:7px;text-align:center">83.1</td><td style="padding:7px;text-align:center">84.6</td></tr> <tr><td style="padding:7px">SciCode</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">42.2</td><td style="padding:7px;text-align:center">42.0</td><td style="padding:7px;text-align:center">48.8</td><td style="padding:7px;text-align:center">50.5</td><td style="padding:7px;text-align:center">45.4</td><td style="padding:7px;text-align:center">49.2</td><td style="padding:7px;text-align:center">54.5</td><td style="padding:7px;text-align:center">53.5</td></tr> <tr><td colspan="9" style="padding:8px 12px;font-weight:600;color:#3248AF;background:rgba(50,72,175,.07)">Long Context &amp; Scientific Reasoning</td></tr> <tr><td style="padding:7px">AA-LCR</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">73.0</td><td style="padding:7px;text-align:center">72.7</td><td style="padding:7px;text-align:center">74.7</td><td style="padding:7px;text-align:center">76.7</td><td style="padding:7px;text-align:center">80.3</td><td style="padding:7px;text-align:center">75.3</td><td style="padding:7px;text-align:center">75.3</td><td style="padding:7px;text-align:center">73.0</td></tr> <tr><td style="padding:7px">Humanity's Last Exam</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">33.6</td><td style="padding:7px;text-align:center">29.0</td><td style="padding:7px;text-align:center">40.5</td><td style="padding:7px;text-align:center">41.1</td><td style="padding:7px;text-align:center">39.0</td><td style="padding:7px;text-align:center">41.0</td><td style="padding:7px;text-align:center">42.3</td><td style="padding:7px;text-align:center">48.7</td></tr> <tr><td style="padding:7px">GPQA Diamond</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">87.6</td><td style="padding:7px;text-align:center">89.3</td><td style="padding:7px;text-align:center">92.3</td><td style="padding:7px;text-align:center">89.5</td><td style="padding:7px;text-align:center">92.9</td><td style="padding:7px;text-align:center">92.8</td><td style="padding:7px;text-align:center">91.4</td><td style="padding:7px;text-align:center">92.0</td></tr> <tr><td style="padding:7px">CritPt</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">10.9</td><td style="padding:7px;text-align:center">1.7</td><td style="padding:7px;text-align:center">13.4</td><td style="padding:7px;text-align:center">20.9</td><td style="padding:7px;text-align:center">3.7</td><td style="padding:7px;text-align:center">18.0</td><td style="padding:7px;text-align:center">12.0</td><td style="padding:7px;text-align:center">20.9</td></tr> <tr><td colspan="9" style="padding:8px 12px;font-weight:600;color:#3248AF;background:rgba(50,72,175,.07)">Knowledge Reliability</td></tr> <tr><td style="padding:7px">AA-Omniscience Accuracy</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">33.5</td><td style="padding:7px;text-align:center">30.8</td><td style="padding:7px;text-align:center">31.1</td><td style="padding:7px;text-align:center">24.3</td><td style="padding:7px;text-align:center">16.7</td><td style="padding:7px;text-align:center">49.1</td><td style="padding:7px;text-align:center">48.9</td><td style="padding:7px;text-align:center">48.8</td></tr> <tr><td style="padding:7px">Non-Hallucination Rate</td><td style="padding:7px;text-align:center;font-weight:700;color:#3248AF;background:rgba(50,72,175,.05)">11.9</td><td style="padding:7px;text-align:center">11.1</td><td style="padding:7px;text-align:center">74.4</td><td style="padding:7px;text-align:center">73.7</td><td style="padding:7px;text-align:center">81.6</td><td style="padding:7px;text-align:center">5.2</td><td style="padding:7px;text-align:center">57.7</td><td style="padding:7px;text-align:center">60.7</td></tr> </tbody> </table> </div> <p style="font-size:11px;opacity:.72"> † GDPval-AA v2 uses Artificial Analysis' normalized score, <code>(Elo − 500) / 2000</code>. Non-Hallucination Rate is <code>1 − hallucination rate</code>. Higher is better for every benchmark. Snapshot checked August 18, 2026; values may change as evaluations are updated. </p>

Model Information

License

This repository is licensed under the Apache License 2.0.

Agnes 2.5 Pro Alpha is a post-trained derivative of 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.

Quickstart

<div style="border-left:4px solid #3248AF;background:rgba(50,72,175,.08);border-radius:6px;padding:12px 16px;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;font-size:14px;line-height:1.6"> <div style="font-weight:700;color:#3248AF;margin-bottom:6px">REASONING MODEL</div> <p style="margin:0"><b>Agnes 2.5 Pro Alpha</b> 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.</p> </div> <h3>SGLang</h3>

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

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

pip install openai

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

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

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.

Raise max_tokens if a response stops early.

Model Capabilities

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

@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}
}