curl --request POST \
--url https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent \
--header 'Content-Type: application/json' \
--data '
{
"contents": [
{
"role": "user",
"parts": [
{
"text": "Generate an image of a sunset over mountains"
}
]
}
],
"generationConfig": {
"responseModalities": [
"TEXT",
"IMAGE"
],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "1K"
}
}
}
'import requests
url = "https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent"
payload = {
"contents": [
{
"role": "user",
"parts": [{ "text": "Generate an image of a sunset over mountains" }]
}
],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "1K"
}
}
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
contents: [
{role: 'user', parts: [{text: 'Generate an image of a sunset over mountains'}]}
],
generationConfig: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {aspectRatio: '16:9', imageSize: '1K'}
}
})
};
fetch('https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'contents' => [
[
'role' => 'user',
'parts' => [
[
'text' => 'Generate an image of a sunset over mountains'
]
]
]
],
'generationConfig' => [
'responseModalities' => [
'TEXT',
'IMAGE'
],
'imageConfig' => [
'aspectRatio' => '16:9',
'imageSize' => '1K'
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent"
payload := strings.NewReader("{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"Generate an image of a sunset over mountains\"\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"TEXT\",\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"1K\"\n }\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent")
.header("Content-Type", "application/json")
.body("{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"Generate an image of a sunset over mountains\"\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"TEXT\",\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"1K\"\n }\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"Generate an image of a sunset over mountains\"\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"TEXT\",\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"1K\"\n }\n }\n}"
response = http.request(request)
puts response.read_body{
"candidates": [
{
"content": {
"role": "model",
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
}
}
]
}
}
],
"usageMetadata": {
"trafficType": "ON_DEMAND"
},
"modelVersion": "gemini-2.5-flash-image"
}{
"error": {
"code": 123,
"message": "<string>",
"status": "<string>"
}
}{
"error": {
"code": 123,
"message": "<string>",
"status": "<string>"
}
}{
"error": {
"code": 123,
"message": "<string>",
"status": "<string>"
}
}gemini-2.5-flash-image-stream
Generate images using Gemini 2.5 Flash Image in streaming mode via SSE. The endpoint returns newline-delimited Server-Sent Events. Thinking chunks (thought=true) arrive first, followed by the image chunk containing inlineData, and finally a usage metadata chunk.
curl --request POST \
--url https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent \
--header 'Content-Type: application/json' \
--data '
{
"contents": [
{
"role": "user",
"parts": [
{
"text": "Generate an image of a sunset over mountains"
}
]
}
],
"generationConfig": {
"responseModalities": [
"TEXT",
"IMAGE"
],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "1K"
}
}
}
'import requests
url = "https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent"
payload = {
"contents": [
{
"role": "user",
"parts": [{ "text": "Generate an image of a sunset over mountains" }]
}
],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "1K"
}
}
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
contents: [
{role: 'user', parts: [{text: 'Generate an image of a sunset over mountains'}]}
],
generationConfig: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {aspectRatio: '16:9', imageSize: '1K'}
}
})
};
fetch('https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'contents' => [
[
'role' => 'user',
'parts' => [
[
'text' => 'Generate an image of a sunset over mountains'
]
]
]
],
'generationConfig' => [
'responseModalities' => [
'TEXT',
'IMAGE'
],
'imageConfig' => [
'aspectRatio' => '16:9',
'imageSize' => '1K'
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent"
payload := strings.NewReader("{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"Generate an image of a sunset over mountains\"\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"TEXT\",\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"1K\"\n }\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent")
.header("Content-Type", "application/json")
.body("{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"Generate an image of a sunset over mountains\"\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"TEXT\",\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"1K\"\n }\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://www.anyfast.ai/v1beta/models/gemini-2.5-flash-image:streamGenerateContent")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"text\": \"Generate an image of a sunset over mountains\"\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"TEXT\",\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"1K\"\n }\n }\n}"
response = http.request(request)
puts response.read_body{
"candidates": [
{
"content": {
"role": "model",
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
}
}
]
}
}
],
"usageMetadata": {
"trafficType": "ON_DEMAND"
},
"modelVersion": "gemini-2.5-flash-image"
}{
"error": {
"code": 123,
"message": "<string>",
"status": "<string>"
}
}{
"error": {
"code": 123,
"message": "<string>",
"status": "<string>"
}
}{
"error": {
"code": 123,
"message": "<string>",
"status": "<string>"
}
}Query Parameters
API Key
Set to sse for explicit Server-Sent Events mode (optional, streaming is the default behaviour of this endpoint)
sse Body
Array of conversation turns. Each turn has a role and parts. A part can be a text prompt, or an inline_data image (base64). To use a reference image, include both a text part and an inline_data part in the same parts array.
Show child attributes
Show child attributes
[
{
"role": "user",
"parts": [
{
"text": "Generate an image of a sunset over mountains"
}
]
}
]Show child attributes
Show child attributes
Response
Streaming SSE response. Each line starts with "data:" followed by a JSON chunk. Three chunk types are delivered in order: (1) Thinking chunks — parts[0].thought is true; (2) Image chunk — parts[0].inlineData contains mimeType and base64 data (camelCase); (3) Final usage chunk — top-level usageMetadata with thoughtsTokenCount.
A single SSE chunk. Three variants are possible: thinking chunk (parts[].thought=true), image chunk (parts[].inlineData), or usage chunk (no candidates).
Present in thinking and image chunks; absent in the final usage chunk.
Show child attributes
Show child attributes
Token usage. Final chunk contains full details including thoughtsTokenCount.
Show child attributes
Show child attributes
"gemini-2.5-flash-image"
"2025-01-01T00:00:00Z"
"abc123"
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