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Agnes 3.0 Flash
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Agnes 3.0 Flash

agnes-3.0-flashllms.txt
Agnes
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Agnes 3.0 Flash is designed for real-world agent tasks and development workflows, covering the full execution chain from task understanding and planning to tool invocation and final delivery. The model focuses on improving stability, instruction-following, factual grounding, and output completeness in complex tasks, helping developers build more reliable agent applications.

Pricing

  • Input Tokens: $0.03 /M tokens
  • Output Tokens: $0.15 /M tokens

Input Modalities

  • Text
  • Vision

Output Modalities

  • Text

Capabilities

  • Thinking
  • Streaming
  • Tool calling
  • Web search
  • URL context
  • Code interpreter
  • Computer use
  • File search
  • Memory tool
  • Structured outputs
  • Citations
  • Prompt caching
  • Background mode
  • Server-side sessions

Providers

Agnes agnes-3.0-flash
Pricing$0.03$0.15
Context512K
Max output65K
Latency8.7S
Throughput33.7TPS
Uptime
0.00% uptime 2 days ago
100.00% uptime yesterday
100.00% uptime today

Performance for agnes-3.0-flash

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="agnes-3.0-flash",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is Agnes 3.0 Flash?

Agnes 3.0 Flash is designed for real-world agent tasks and development workflows, covering the full execution chain from task understanding and planning to tool invocation and final delivery. The model focuses on improving stability, instruction-following, factual grounding, and output completeness in complex tasks, helping developers build more reliable agent applications.
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