Gemini 3 Flash is a fast, multimodal language model for real-time text generation. Supports text and image inputs, function calling, and Google Search grounding. Token-based pricing: $0.30/M input tokens and $1.80/M output tokens. Two endpoints: standard async (/gemini-3-flash) and live streaming (/gemini-3-flash/stream) via SSE.
About this model
Gemini 3 Flash is a high-speed, multimodal language model built for real-time text generation. It handles text and image inputs natively, supports function calling and Google Search grounding, and delivers low-latency responses β making it ideal for chatbots, assistants, content tools, and automation pipelines. Pricing is token-based: $0.30 per million input tokens and $1.80 per million output tokens.
Cost analysis
| Provider | Cost | Notes |
|---|---|---|
| Google AI (Official) | $0.50/M input tokens, $3.00/M output tokens | Official Google AI pricing for Gemini Flash. muapiapp is 40% cheaper β you save $0.20 per million input tokens and $1.20 per million output tokens. |
| muapiapp | $0.30/M input tokens, $1.80/M output tokens | 40% cheaper than Google's official pricing. Token-based billing β you only pay for what you use, with no per-request minimums or setup fees. |
| Fal.ai | Not available | Fal.ai does not currently offer Gemini Flash as a standalone LLM endpoint. |
| Replicate | Not available | Replicate does not currently offer Gemini Flash as a hosted model. |
Official Google AI pricing for Gemini Flash. muapiapp is 40% cheaper β you save $0.20 per million input tokens and $1.20 per million output tokens.
40% cheaper than Google's official pricing. Token-based billing β you only pay for what you use, with no per-request minimums or setup fees.
Fal.ai does not currently offer Gemini Flash as a standalone LLM endpoint.
Replicate does not currently offer Gemini Flash as a hosted model.
* Competitor pricing is estimated based on similar model architectures and usage tiers.
Configuration schema
| Parameter | Type | Description | Default |
|---|---|---|---|
| Prompt | string | The user message or instruction for the model. | Summarize the key points of the attached image. |
| Image URL | string | Optional image URL to include as multimodal input. | undefined |
| System Prompt | string | Optional system-level instruction to guide model behavior. | You are a helpful assistant that responds concisely. |
The user message or instruction for the model.
Summarize the key points of the attached image.Optional image URL to include as multimodal input.
undefinedOptional system-level instruction to guide model behavior.
You are a helpful assistant that responds concisely.Developer documentation
Gemini 3 Flash offers two endpoints β standard async and live streaming.
POST /api/v1/gemini-flashUse for workflows, automation, and batch processing.
Python:
import requests, time
API_KEY = "your_api_key_here"
headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}
# 1. Submit
r = requests.post("https://api.muapi.ai/api/v1/gemini-flash", headers=headers,
json={"prompt": "Explain quantum computing.", "system_prompt": "Be concise."})
request_id = r.json()["request_id"]
# 2. Poll
while True:
result = requests.get(f"https://api.muapi.ai/api/v1/predictions/{request_id}/result", headers=headers).json()
if result["status"] == "completed":
print(result["output"]["text"])
break
time.sleep(2)
cURL:
# Submit
curl -X POST https://api.muapi.ai/api/v1/gemini-flash \
-H "x-api-key: YOUR_API_KEY" -H "Content-Type: application/json" \
-d '{"prompt": "Explain quantum computing."}'
# Poll
curl https://api.muapi.ai/api/v1/predictions/{request_id}/result \
-H "x-api-key: YOUR_API_KEY"
POST /api/v1/gemini-flash/streamUse for chat UIs and real-time token display.
Python (httpx):
import httpx, json
API_KEY = "your_api_key_here"
with httpx.Client(timeout=120) as client:
with client.stream("POST", "https://api.muapi.ai/api/v1/gemini-flash/stream",
headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
json={"prompt": "Write a poem about the ocean."}
) as response:
for line in response.iter_lines():
if line.startswith("data: "):
data = line[6:]
if data == "[DONE]": break
delta = json.loads(data)["choices"][0]["delta"].get("content", "")
if delta: print(delta, end="", flush=True)
print()
JavaScript (fetch):
const response = await fetch("https://api.muapi.ai/api/v1/gemini-flash/stream", {
method: "POST",
headers: { "x-api-key": "YOUR_API_KEY", "Content-Type": "application/json" },
body: JSON.stringify({ prompt: "Write a poem about the ocean." })
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
while (true) {
const { done, value } = await reader.read();
if (done) break;
for (const line of decoder.decode(value, { stream: true }).split("\n")) {
if (!line.startsWith("data: ")) continue;
const data = line.slice(6).trim();
if (data === "[DONE]") return;
const delta = JSON.parse(data).choices?.[0]?.delta?.content ?? "";
if (delta) process.stdout.write(delta);
}
}
cURL:
curl -X POST https://api.muapi.ai/api/v1/gemini-flash/stream \
-H "x-api-key: YOUR_API_KEY" -H "Content-Type: application/json" \
-d '{"prompt": "Write a poem about the ocean."}' --no-buffer
See full documentation at /docs/streaming.
| Token Type | Rate |
|---|---|
| Input | $0.30 per million |
| Output | $1.80 per million |
A minimum wallet balance of $1.00 is required.
Frequently asked
It supports text prompts and optional image URLs. All media types use the same structure in the payload.
Pricing is token-based: $0.30 per million input tokens and $1.80 per million output tokens. The actual cost is calculated from the API response and deducted from your wallet after each call.
Since cost is deducted after the call based on token usage, a $1.00 minimum ensures your wallet can cover the actual usage cost.
Yes. Gemini 3 Flash supports OpenAI-compatible function calling. Define your tools in the request and the model will invoke them as needed.
/gemini-3-flash is the standard async endpoint β it returns a request_id and you poll for the result. /gemini-3-flash/stream returns a live Server-Sent Events (SSE) stream, delivering tokens as they are generated. Use streaming for chat UIs; use the standard endpoint for workflows and automation.