Speed
Output tokens / second
Custom pricing for production workloads
Gemini 3.6 Flash is Google's most intelligent Flash model, balancing speed with frontier intelligence for strong performance on agentic, coding and multimodal tasks, with superior search and grounding.
Gemini 3.5 Flash model narrows the gap between Flash-tier speed and Pro-level reasoning.
google/gemini-3.6-flashhttps://api.aimlapi.com/v1With Gemini 3.5 Flash API, Google introduces a new generation of multimodal AI designed for real-world production workloads, high-speed automation, advanced coding assistance, and scalable agentic workflows. Whether you're building AI-powered SaaS tools, enterprise copilots, customer support systems, automation pipelines, or next-generation coding agents, Gemini 3.5 Flash delivers the performance modern applications demand.
Unlike traditional heavyweight models that often sacrifice latency for reasoning quality, Gemini 3.5 Flash focuses on delivering advanced reasoning, coding capability, and multimodal understanding at Flash-level speed.
Everything developers are expecting based on current leaks, official Gemini 3 series documentation, and early tester reports.
Expected to come significantly closer to Gemini 3.1 Pro's reasoning quality while remaining faster and cheaper. Early testers report that the long-standing "lazy model" problem has been largely addressed.
Carries forward the full 1M token input context window from the Gemini 3 generation, large enough to fit entire codebases, lengthy documents, and complex multi-turn agentic conversations in a single call.
Like Gemini 3 Flash before it, 3.5 Flash is expected to support four thinking levels, minimal, low, medium, and high, letting developers trade off reasoning depth against latency and cost per request.
Accepts text, images, audio, video, and PDFs as input. Carries forward the improved audio grounding from the Gemini 3 series, including better handling of accented speech and background noise.
Agentic workflows are a documented use case for Gemini 3.6 Flash, including multi-step orchestration and tool use.
Complex video reasoning is also listed as a use case, with the model described as suited to difficult multimodal reasoning tasks.
Coding tasks are explicitly called out, including coding work, full-stack code refactoring, and code migrations.
Enterprise workflows are another stated use case, with documentation describing the model as well-suited for enterprise tasks and knowledge work.
See how Gemini 3.6 Flash compares in generation speed and latency. Data retrieved Nov 3, 2026.
Output tokens / second
Compare 6 models available on AI/ML API across 5 independent benchmarks.
Expert-written questions across academic subjects.
Graduate-level science questions written to resist web search.
Agentic tasks completed in a command-line environment.
Reasoning across long documents of about 100k tokens.
Python coding tasks drawn from scientific research.
| Usage | Price | Unit |
|---|---|---|
| Input | $1.5 | 1M tokens |
| Output | $7.5 | 1M tokens |
| Cached input | $0.15 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
| $1.5 | $7.5 | |
| $0.3 | $2.5 | |
| $0.75 | $3.75 | |
| $0.75 | $3.75 | |
| $2 | $10 |
Give your agent the model details, code and documentation.
export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "google/gemini-3.6-flash",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "google/gemini-3.6-flash",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "google/gemini-3.6-flash",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Gemini 3.6 Flash has a 1,048,576 tokens context window and can return up to 65,536 tokens.
Gemini 3.6 Flash takes image, text as input and returns text.
Use google/gemini-3.6-flash as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Gemini 3.6 Flash became available on July 21, 2026.
Gemini 3.6 Flash costs $1.5 input and $7.5 output per 1M tokens on AI/ML API, billed as you go with no subscription.
Yes, Gemini 3.6 Flash can stream responses as they are generated.
Yes, Gemini 3.6 Flash accepts image input alongside text.
Gemini 3.6 Flash was built by Google.
Yes, it includes reasoning as one of its supported features and capabilities.
No, it is accessed through Google's hosted API endpoint rather than run locally.