Google's Gemini 2.5 Flash is available through InferAll — no Google Cloud project, no separate API key, no per-project billing setup. Your ifu_... key routes to Gemini the same way it routes to GPT-4.1, Claude Sonnet, and free NVIDIA models.
Gemini 2.5 Flash pricing via InferAll:
| Model | Input | Output |
|---|---|---|
gemini-2.5-flash |
$0.15/M | $0.60/M |
gemini-2.5-pro |
$1.25/M | $10.00/M |
Google's published rates, zero markup.
OpenAI SDK (drop-in)
Using a new key? A no-card trial key routes to the free NIM models only, so the paid model in these snippets returns a 402 until you add a card. To get a working first call right now, keep everything below and swap the model to
meta/llama-3.1-8b-instruct.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferall.ai/v1",
api_key="ifu_your_key_here", # get one free at inferall.ai/keys
)
response = client.chat.completions.create(
model="gemini/gemini-2.5-flash",
messages=[
{"role": "user", "content": "Summarize the key differences between REST and GraphQL."}
],
max_tokens=512,
)
print(response.choices[0].message.content)
No changes to your existing OpenAI code — just point base_url at InferAll.
TypeScript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.inferall.ai/v1",
apiKey: process.env.INFERALL_API_KEY,
});
const response = await client.chat.completions.create({
model: "gemini/gemini-2.5-flash",
messages: [{ role: "user", content: "Write a regex to validate an email address." }],
max_tokens: 256,
});
console.log(response.choices[0].message.content);
LangChain
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="gemini/gemini-2.5-flash",
openai_api_base="https://api.inferall.ai/v1",
openai_api_key="ifu_your_key_here",
)
response = llm.invoke("What is the capital of France?")
print(response.content)
Vision (multimodal)
Gemini 2.5 Flash supports images. Pass base64-encoded images in the message content:
import base64
with open("screenshot.png", "rb") as f:
image_data = base64.b64encode(f.read()).decode()
response = client.chat.completions.create(
model="gemini/gemini-2.5-flash",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "What's wrong with this UI?"},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}},
],
}],
max_tokens=512,
)
When to use Gemini 2.5 Flash
Gemini 2.5 Flash is in the same cost tier as GPT-4o-mini ($0.15/$0.60) but with a much larger default context window and strong performance on structured extraction, summarization, and multilingual tasks. It's a good default for:
- High-volume pipelines where per-request cost matters
- Document processing — long-context summarization, extraction
- Multilingual apps — strong coverage across languages
- Vision tasks — image understanding, chart analysis
When you need stronger reasoning, step up to gemini-2.5-pro (still via the same key and endpoint).
Why not use Google AI Studio directly?
You can — but you'd manage a separate Google API key, a separate billing account, and separate client configuration for every project. InferAll gives you one key that routes to Gemini, Claude, GPT-4.1, and free NVIDIA models. Switch between providers by changing one string. Useful when you're benchmarking, building fallback chains, or running multiple projects.
Sign up at inferall.ai/keys, then activate via the $5 starter pack at /billing — the $5 becomes spendable balance for premium providers (Gemini 2.5 Flash bills at the published rate with zero markup) and unlocks the 40+ NIM open models at $0 in/out (within the free-plan daily request caps).