Google: Gemma 3 12B
google/gemma-3-12b-it
Access Gemma 3 12B 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/gemma-3-12b-it"
}).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/gemma-3-12b-it"
}).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/gemma-3-12b-it",
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/gemma-3-12b-it",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Gemma 3 12B Instruct is Google's mid-sized open multimodal model supporting text and image input with a 128K token context window. It supports 140+ languages and offers strong performance for single-GPU deployment.
Context Window 131K
tokens
Max Output 16K
tokens
Input Cost $0.05
per million tokens
Output Cost $0.15
per million tokens
Release Date Mar 13, 2025
Model Playground
Try Gemma 3 12B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Gemma 3 12B performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 34.9% |
| Humanity's Last Exam Cross-domain reasoning | 4.8% |
| LiveCodeBench Recent coding problems | 13.7% |
| SciCode Scientific programming | 17.4% |
| MATH-500 Competition math | 85.3% |
| AIME 2024 Advanced math exam | 22.0% |
| AIME 2025 Advanced math exam | 18.3% |
| IFBench Instruction following | 36.7% |
| LCR Long-context reasoning | 6.7% |
| Terminal-Bench Hard Agentic terminal tasks | 0.8% |
| τ²-Bench Tool use / agents | 10.8% |
Scores sourced from Artificial Analysis.
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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 Gemma 3 12B 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 Gemma 3 12B 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 | $0.05 |
| Output | $0.15 |
Gemma 3 12B was created by Google and released on Mar 13, 2025.
Gemma 3 12B supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Gemma 3 12B can generate up to 16K tokens in a single response.
Gemma 3 12B scores 5.5 on the Artificial Analysis Intelligence Index, outperforming 17% of tracked models. On coding, it scores 5.8 (outperforms 6% of models). On math, it scores 18.3 (outperforms 20% of models).
Yes — the Gemma 3 12B 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 Gemma 3 12B to your app without worrying about API keys or setup.
Read the Docs View Tutorials