Google: Gemini 2.0 Flash
google/gemini-2.0-flash
Access Gemini 2.0 Flash from Google using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-2.0-flash"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-2.0-flash"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "google/gemini-2.0-flash",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Gemini 2.0 Flash is Google's fast multimodal model with native tool use, 1M token context window, and support for text, images, video, and audio input. It's optimized for agentic workflows with low latency and cost-efficient inference.
Context Window 131K
tokens
Max Output 8K
tokens
Input Cost $0.1
per million tokens
Output Cost $0.4
per million tokens
Input text, image, audio, video, pdf
modalities
Tool Use Yes
Knowledge Cutoff Jun 2024
Release Date Dec 11, 2024
Model Playground
Try Gemini 2.0 Flash instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Gemini 2.0 Flash performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 62.3% |
| Humanity's Last Exam Cross-domain reasoning | 5.3% |
| LiveCodeBench Recent coding problems | 33.4% |
| SciCode Scientific programming | 33.3% |
| MATH-500 Competition math | 93.0% |
| AIME 2024 Advanced math exam | 33.0% |
| AIME 2025 Advanced math exam | 21.7% |
| IFBench Instruction following | 40.2% |
| LCR Long-context reasoning | 28.3% |
| Terminal-Bench Hard Agentic terminal tasks | 3.8% |
| τ²-Bench Tool use / agents | 29.5% |
Scores sourced from Artificial Analysis.
Find other Google models →
Gemini 3.1 Flash Lite Image
Gemini 3.1 Flash Lite Image is Google DeepMind's fastest and lowest-cost image generation and editing model in the Gemini 3.1 family, also known as Nano Banana 2 Lite. It generates a 1K-resolution image in about 4 seconds, roughly 2.7x faster than Gemini 3.1 Flash Image, at $0.0336 per image. It keeps the character consistency, precise natural-language editing, and real-world knowledge of the Nano Banana family while trading some peak quality for speed. It supports text-to-image generation, image editing, and multi-image composition through the same API used by other Gemini image models, with a discrete set of 14 aspect ratios at 1K resolution. It's built for high-volume, latency-sensitive use cases such as rapid prototyping, interactive applications, and batch image generation, where cost and turnaround time matter more than maximum fidelity.
ChatGemini 3.5 Flash
Gemini 3.5 Flash is Google DeepMind's frontier-speed model that combines Flash-tier latency and cost with near-Pro-level reasoning, announced at Google I/O 2026. It processes output 4x faster than comparable frontier models while outperforming Gemini 3.1 Pro on coding and agentic benchmarks — 76.2% on Terminal-Bench 2.1, 83.6% on MCP Atlas, and 84.2% on CharXiv Reasoning. It's purpose-built for agentic workflows: orchestrating multi-step tool use, long-context document analysis, and iterative code generation. With a 1M token context window and full multimodal input support (text, image, audio, video, PDF), it handles complex real-world tasks at scale. At $1.50 per million input tokens and $9.00 per million output tokens, it's the best choice for developers who need frontier intelligence without frontier latency or cost.
ChatGemma 4 26B A4B
Gemma 4 26B A4B is a Mixture-of-Experts (MoE) open model from Google DeepMind, built from the same research as Gemini 3. It has 26B total parameters but activates only 3.8B per forward pass, delivering near-31B-dense quality at a fraction of the compute cost. The model supports a 256K token context window, multimodal image and text input, built-in step-by-step reasoning (thinking mode), and native function calling for agentic workflows. It currently ranks #6 among open models on the Arena AI text leaderboard with an estimated LMArena score of 1441 — competitive with models many times its active size. It excels at reasoning, coding, long-context tasks, and structured tool use. It's a strong pick for developers who need high throughput and low latency without sacrificing capability.
Frequently Asked Questions
You can access Gemini 2.0 Flash by Google through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Gemini 2.0 Flash to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.1 |
| Output | $0.4 |
Gemini 2.0 Flash was created by Google and released on Dec 11, 2024.
Gemini 2.0 Flash supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Gemini 2.0 Flash can generate up to 8K tokens in a single response.
Gemini 2.0 Flash has a knowledge cutoff date of Jun 2024. This means the model was trained on data available up to that date.
Gemini 2.0 Flash accepts the following input types: text, image, audio, video, pdf. It produces: text.
Yes, Gemini 2.0 Flash supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Gemini 2.0 Flash scores 12.3 on the Artificial Analysis Intelligence Index, outperforming 42% of tracked models. On math, it scores 21.7 (outperforms 23% of models).
Yes — the Gemini 2.0 Flash API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Gemini 2.0 Flash to your app without worrying about API keys or setup.
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