Agnes 2.5 Pro

Agnes 2.5 Pro is the commercial stable version of the benchmark-listed Agnes 2.5 Pro Alpha model. It is designed for advanced coding, scientific reasoning, long-context analysis, agentic workflows, and multimodal understanding. The model is available through the OpenAI-compatible Chat Completions API.

Model

agnes-2.5-pro

API Endpoints

Chat Completions: POST /v1/chat/completions
Responses: POST /v1/responses

Model Type

Paid reasoning model with text and image input.

Benchmark Reference

Based on the Artificial Analysis listing for Agnes 2.5 Pro Alpha.

Overview

Agnes 2.5 Pro is intended for production workloads that need stronger reasoning depth than flash-class models, including complex code tasks, science and math reasoning, long-context understanding, knowledge-intensive Q&A, and agentic terminal or workflow tasks. It uses the same base integration pattern as other Agnes text models:

Item	Value
Base URL	`https://api.agnes-ai.cn/v1`
Endpoint	`POST /v1/chat/completions`
Model name	`agnes-2.5-pro`
Input modalities	Text and image URL
Output modality	Text
Release date	August 1, 2026
Billing	Paid model

Artificial Analysis lists the Agnes 2.5 Pro Alpha benchmark model as a proprietary reasoning model released on July 24, 2026. Agnes 2.5 Pro is the commercial stable version of that benchmark-listed model. External benchmark scores are third-party references and may change as Artificial Analysis updates its methodology or leaderboard data.

Core Capabilities

Advanced Reasoning

Suitable for scientific reasoning, knowledge-intensive questions, and multi-step analysis.

Coding and Terminal Tasks

Designed for code generation, debugging, refactoring, test generation, and agentic coding workflows.

Long-context Analysis

Handles long documents, structured context, and multi-turn reasoning tasks.

Image Understanding

Accepts image URL inputs for visual analysis and multimodal reasoning.

Tool Calling

Supports OpenAI-compatible function calling and external tool orchestration.

Streaming

Supports streaming responses for interactive product experiences.

Artificial Analysis Results

The following metrics are based on the Artificial Analysis data provided for the benchmark-listed Agnes 2.5 Pro Alpha model.

Summary Item	Value
Intelligence summary rank	`#9 / 153`
Price summary rank	`#46 / 153`
Cache hit price summary rank	`#8 / 153`

Metric	Score
Intelligence Index	`39`
GPQA	`87.6%`
SciCode	`42.2%`
LCR	`63.7%`
Omniscience	`-26.3`
Accuracy	`32.3%`
Non-hallucination	`13.3%`
CritPt	`10.9%`
HLE	`31.9%`
TerminalBench v2.1	`67.0%`
GDPval v2	`1170 elo`
Tau3	`11.6%`

Artificial Analysis Model Page

View the benchmark model profile, summary, and pricing analysis.

Artificial Analysis Leaderboard

Compare the benchmark-listed Agnes 2.5 Pro Alpha model with other listed models.

API Reference

Endpoint

POST https://api.agnes-ai.cn/v1/chat/completions

Headers

-H "Authorization: Bearer YOUR_API_KEY"
-H "Content-Type: application/json"

Request Parameters

Parameter	Type	Required	Description
`model`	string	Yes	Model name. Use `agnes-2.5-pro`.
`messages`	array	Yes	Conversation messages, including `system`, `user`, and `assistant` messages.
`messages[].content`	string / array	Yes	Message content. It can be plain text or an array of content blocks containing `text` and `image_url`.
`temperature`	number	No	Controls randomness. Lower values produce more deterministic output.
`top_p`	number	No	Controls nucleus sampling.
`max_tokens`	number	No	Maximum number of tokens to generate in the response.
`stream`	boolean	No	Whether to enable streaming output.
`tools`	array	No	Tool definitions for tool-calling workflows.
`tool_choice`	string / object	No	Controls whether and how the model uses tools.
`chat_template_kwargs`	object	No	Extension field for OpenAI-compatible requests.
`thinking`	object	No	Field for enabling Thinking mode in Anthropic-compatible requests.

Image URL Input

Agnes 2.5 Pro supports text and image URL inputs in the same messages request.

{
  "role": "user",
  "content": [
    {
      "type": "text",
      "text": "Analyze this architecture diagram and identify possible failure points."
    },
    {
      "type": "image_url",
      "image_url": {
        "url": "https://example.com/diagram.png"
      }
    }
  ]
}

Request Examples

Basic Chat
Coding
Streaming
Image Understanding

curl https://api.agnes-ai.cn/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.5-pro",
    "messages": [
      {
        "role": "system",
        "content": "You are a precise technical assistant."
      },
      {
        "role": "user",
        "content": "Explain the tradeoffs between optimistic locking and pessimistic locking in distributed systems."
      }
    ],
    "temperature": 0.3,
    "max_tokens": 1200
  }'

curl https://api.agnes-ai.cn/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.5-pro",
    "messages": [
      {
        "role": "user",
        "content": "Review this TypeScript API handler for security issues, explain the risks, and provide a corrected version."
      }
    ],
    "temperature": 0.2,
    "max_tokens": 2000
  }'

curl https://api.agnes-ai.cn/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.5-pro",
    "messages": [
      {
        "role": "user",
        "content": "Create a step-by-step migration plan for moving a monolith to services."
      }
    ],
    "stream": true
  }'

curl https://api.agnes-ai.cn/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.5-pro",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Summarize this chart and call out any anomalies."
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/chart.png"
            }
          }
        ]
      }
    ]
  }'

Response Format

{
  "id": "chatcmpl_xxx",
  "object": "chat.completion",
  "created": 1784899200,
  "model": "agnes-2.5-pro",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 120,
    "completion_tokens": 300,
    "total_tokens": 420
  }
}

Response Fields

Field	Type	Description
`id`	string	Unique ID of the completion request.
`object`	string	Object type, usually `chat.completion`.
`created`	integer	Request timestamp.
`model`	string	Model used for the request.
`choices`	array	List of generated responses.
`choices[].message.role`	string	Role of the message sender.
`choices[].message.content`	string	Content generated by the model.
`choices[].finish_reason`	string	Reason generation stopped.
`usage`	object	Token usage information.

Responses API

In addition to Chat Completions, this model supports the OpenAI Responses API. Use input instead of messages.

Responses endpoint

POST https://api.agnes-ai.cn/v1/responses

Responses request parameters

Parameter	Type	Required	Description
`model`	string	Yes	Model name. Use `agnes-2.5-pro`.
`input`	string / array	Yes	A plain text prompt or an array of structured input messages.
`max_output_tokens`	integer	No	Maximum output budget. Use a larger value for reasoning models to avoid an `incomplete` response.

Text Input
Structured Input

curl https://api.agnes-ai.cn/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.5-pro",
    "input": "Explain how autonomous agents use tools.",
    "max_output_tokens": 1024
  }'

curl https://api.agnes-ai.cn/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "agnes-2.5-pro",
    "input": [
      {
        "role": "user",
        "content": [
          {
            "type": "input_text",
            "text": "Explain how autonomous agents use tools."
          }
        ]
      }
    ],
    "max_output_tokens": 1024
  }'

Responses output format

{
  "id": "resp_xxx",
  "object": "response",
  "status": "completed",
  "model": "agnes-2.5-pro",
  "output": [
    {
      "type": "reasoning",
      "summary": []
    },
    {
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Autonomous agents use tools to retrieve data and perform actions."
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 40,
    "output_tokens": 80,
    "total_tokens": 120
  },
  "error": null,
  "incomplete_details": null
}

Field	Type	Description
`id`	string	Unique response ID.
`object`	string	Object type, usually `response`.
`status`	string	Response state, such as `completed` or `incomplete`.
`output`	array	Ordered response items, including reasoning and assistant messages.
`output[].type`	string	Item type, such as `reasoning` or `message`.
`output[].content[].type`	string	Content type. Generated text uses `output_text`.
`output[].content[].text`	string	Generated assistant text.
`usage`	object	Token usage information.
`error`	object / null	Error details when the request fails.
`incomplete_details`	object / null	Explains why a response stopped before completion.

The current response does not include a top-level output_text convenience field. Extract generated text from message items where output[].type is message and output[].content[].type is output_text.

Reasoning items are optional and can use either content[].reasoning_text or summary[].summary_text. Token usage field names can also vary by model: support both input_tokens / output_tokens and prompt_tokens / completion_tokens.

If status is incomplete, inspect incomplete_details and retry with a larger max_output_tokens value. Reasoning models can consume part of the output budget before producing assistant text.

Limits and Pricing

Agnes 2.5 Pro is a paid commercial stable model. Usage is billed by cache read, input, and output tokens.

Item	Value
Context window	`1M` tokens
Max output	`65536` tokens
Input modalities	Text, image
Output modalities	Text
Reasoning	Yes
Model weights	Proprietary, not open source

Type	USD Price
Input cache hit / Cache Read	`$0.0038 / M tokens`
Input cache miss / Input	`$0.45 / M tokens`
Output	`$0.90 / M tokens`

Pricing and availability may vary by account, region, billing configuration, or later pricing updates. Use the Agnes AI platform dashboard as the source of truth for your account.

Best Practices

Reasoning-heavy Tasks

Use Agnes 2.5 Pro for tasks where correctness and multi-step reasoning matter more than raw latency, such as scientific reasoning, complex policy analysis, and long-form technical planning.

Coding Tasks

Provide the target language, framework, existing code, error messages, expected behavior, and constraints. Ask for root-cause analysis before the patch when debugging complex issues.

Long-context Tasks

Use structured sections, filenames, or document labels inside the prompt so the model can refer to sources and produce traceable conclusions.

Integration Checklist

Use agnes-2.5-pro as the model name.

Confirm that your account has access to the paid Agnes 2.5 Pro commercial stable model.

Basic chat completion requests must include model and messages.

Use publicly accessible image_url values for image inputs.

Track cache read, input, and output token usage because this is a paid model.

Get Started

Use Guides

Model

API Endpoints

Model Type

Benchmark Reference

Overview

Core Capabilities

Advanced Reasoning

Coding and Terminal Tasks

Long-context Analysis

Image Understanding

Tool Calling

Streaming

Artificial Analysis Results

Artificial Analysis Model Page

Artificial Analysis Leaderboard

API Reference

Endpoint

Headers

Request Parameters

Image URL Input

Request Examples

Response Format

Response Fields

Responses API

Responses endpoint

Responses request parameters

Responses output format

Limits and Pricing

Best Practices

Integration Checklist

Model

API Endpoints

Model Type

Benchmark Reference

​Overview

​Core Capabilities

Advanced Reasoning

Coding and Terminal Tasks

Long-context Analysis

Image Understanding

Tool Calling

Streaming

​Artificial Analysis Results

Artificial Analysis Model Page

Artificial Analysis Leaderboard

​API Reference

​Endpoint

​Headers

​Request Parameters

​Image URL Input

​Request Examples

​Response Format

​Response Fields

​Responses API

​Responses endpoint

​Responses request parameters

​Responses output format

​Limits and Pricing

​Best Practices

​Integration Checklist

Overview

Core Capabilities

Artificial Analysis Results

API Reference

Endpoint

Headers

Request Parameters

Image URL Input

Request Examples

Response Format

Response Fields

Responses API

Responses endpoint

Responses request parameters

Responses output format

Limits and Pricing

Best Practices

Integration Checklist