Flex inference

The Gemini Flex API is an inference tier that offers a 50% cost reduction compared to standard rates, in exchange for variable latency and best-effort availability. It's designed for latency-tolerant workloads that require synchronous processing but don't need the real-time performance of the standard API.

How to use Flex

To use the Flex tier, specify the service_tier as flex in your request. By default, requests use the standard tier if this field is omitted.

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input="Analyze this dataset for trends...",
    service_tier='flex'
)
print(interaction.output_text)

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

async function main() {
    const interaction = await client.interactions.create({
        model: 'gemini-3.7-flash',
        input: 'Analyze this dataset for trends...',
        service_tier: 'flex'
    });
    console.log(interaction.output_text);
}
await main();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "model": "gemini-3.7-flash",
      "input": "Analyze this dataset for trends...",
      "service_tier": "flex"
  }'

How Flex inference works

Gemini Flex inference bridges the gap between the standard API and the 24-hour turnaround of the Batch API. It utilizes off-peak, "sheddable" compute capacity to provide a cost-effective solution for background tasks and sequential workflows.

Key benefits

• Cost efficiency: Substantial savings for non-production evals, background agents, and data enrichment.
• Low friction: Simply add a single parameter to your existing requests.
• Synchronous workflows: Ideal for sequential API chains where the next request depends on the output of the previous one, making it more flexible than Batch for agentic workflows.

Use cases

• Offline evaluations: Running "LLM-as-a-judge" regression tests or leaderboards.
• Background agents: Sequential tasks like CRM updates, profile building, or content moderation where minutes of delay are acceptable.
• Budget-constrained research: Academic experiments that require high token volume on a limited budget.

Rate limits

Flex inference traffic counts towards your general rate limits; it doesn't offer extended rate limits like the Batch API.

Sheddable capacity

Flex traffic is treated with lower priority. If there is a spike in standard traffic, Flex requests may be preempted or evicted to ensure capacity for high-priority users. If you're looking for high-priority inference, check Priority inference

Error codes

When Flex capacity is unavailable or the system is congested, the API will return standard error codes:

• 503 Service Unavailable: The system is currently at capacity.
• 429 Too Many Requests: Rate limits or resource exhaustion.

Client responsibility