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Gemini 3.5 Flash
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Gemini 3.5 Flash

gemini-3.5-flashllms.txt
Google
Gemini 3.5 Flash provides sustained frontier-level intelligence optimized for real-world tasks at a higher speed and lower cost. Designed for the agentic era, it excels at sub-agent deployment, multi-step workflows, and long-horizon tasks at scale. This model is particularly effective for rapid agentic loops involving complex coding cycles and iterations.

Pricing

PricingCache ReadInput VideoInput AudioWeb SearchCache Storage
$1.5$9
$0.15/M tokens$1.5/M tokens$3/M tokens$0.014/request$1/h/M tokens

Input Modalities

  • Text
  • Vision
  • Audio
  • Video
  • PDF

Output Modalities

  • Text

Context length

  • 1.05M tokens

Max output

  • 65.5K tokens

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

VertexAI gemini-3.5-flash
Pricing$1.5$9
Cache Read$0.15/M tokens
Input Video$1.5/M tokens
Input Audio$3/M tokens
Web Search$0.014/request
Cache Storage$1/h/M tokens
Context1M
Max output64K
Latency6.9S
Throughput115.2TPS
Uptime
56.81% uptime 2 days ago
96.84% uptime yesterday
94.11% uptime today
Google AI Studio gemini-3.5-flash
Pricing$1.5$9
Cache Read$0.15/M tokens
Input Video$1.5/M tokens
Input Audio$3/M tokens
Web Search$0.014/request
Cache Storage$1/h/M tokens
Context1M
Max output64K
Latency6.5S
Throughput147.4TPS
Uptime
20.11% uptime 2 days ago
99.56% uptime yesterday
92.36% uptime today

Performance for gemini-3.5-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="gemini-3.5-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 Gemini 3.5 Flash?

Gemini 3.5 Flash provides sustained frontier-level intelligence optimized for real-world tasks at a higher speed and lower cost. Designed for the agentic era, it excels at sub-agent deployment, multi-step workflows, and long-horizon tasks at scale. This model is particularly effective for rapid agentic loops involving complex coding cycles and iterations.
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