// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemma-3-27b-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-3-27b-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-3-27b-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-3-27b-it",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Gemma 3 27B Instruct is Google's most capable single-GPU open model with multimodal support, 128K context, and 140+ language support. It outperforms many larger models and offers state-of-the-art open-weight performance.
Context Window 131K
tokens
Max Output 16K
tokens
Input Cost $0.08
per million tokens
Output Cost $0.16
per million tokens
Release Date Mar 12, 2025
Output Speed 32
tokens / sec
Latency 0.92s
time to first token
Model Playground
Try Gemma 3 27B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Gemma 3 27B performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 42.8% |
| Humanity's Last Exam Cross-domain reasoning | 4.7% |
| LiveCodeBench Recent coding problems | 13.7% |
| SciCode Scientific programming | 21.2% |
| MATH-500 Competition math | 88.3% |
| AIME 2024 Advanced math exam | 25.3% |
| AIME 2025 Advanced math exam | 20.7% |
| IFBench Instruction following | 31.8% |
| LCR Long-context reasoning | 5.7% |
| Terminal-Bench Hard Agentic terminal tasks | 3.8% |
| τ²-Bench Tool use / agents | 10.5% |
Scores sourced from Artificial Analysis.
Find other Google models →
Gemma 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.
ChatGemma 4 31B
Gemma 4 31B is a dense multimodal model from Google DeepMind, built on the same research foundation as Gemini 3. It is the most capable model in the Gemma 4 family, accepting text, image, and video input with a 256K-token context window. It delivers strong benchmark results: 89.2% on AIME 2026, 85.2% on MMLU Pro, 80.0% on LiveCodeBench v6, and 84.3% on GPQA Diamond. On the Arena AI text leaderboard, it ranks as the #3 open model globally, outperforming many models with far higher parameter counts. Gemma 4 31B features native function calling trained into the model, configurable chain-of-thought reasoning, and structured JSON output — making it especially well-suited for agentic workflows, coding tasks, and multi-turn tool use. It supports over 140 languages and serves as a strong foundation for fine-tuning.
VideoVeo 3.1 Lite
Veo 3.1 Lite is Google DeepMind's most cost-effective video generation model, built for high-volume applications where per-clip cost is a primary concern. It generates video at the same speed as Veo 3.1 Fast but at less than half the price — starting at $0.05 per second for 720p. The model supports text-to-video and image-to-video with 720p and 1080p output in landscape (16:9) or portrait (9:16), at configurable durations of 4, 6, or 8 seconds. It does not support 4K output, scene extension, or native audio generation — clips are silent by default. Veo 3.1 Lite is ideal for developers building batch video pipelines, social media automation, or interactive tools where cost per generation matters most and audio can be added in post-production.
Frequently Asked Questions
You can access Gemma 3 27B 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 27B 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 | $0.08 |
| Output | $0.16 |
Gemma 3 27B was created by Google and released on Mar 12, 2025.
Gemma 3 27B supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Gemma 3 27B can generate up to 16K tokens in a single response.
Yes — the Gemma 3 27B 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 27B to your app without worrying about API keys or setup.
Read the Docs View Tutorials