Gemma 3 27B
VerifiedGoogle's open multimodal model for text and image understanding.
About Gemma 3 27B
The architecture combines vision and language processing in a single transformer-based design. Google trained it to handle interleaved text and image data effectively. Open weights enable inspection, modification, and local deployment by researchers and engineers.
Its strengths lie in flexible multimodal reasoning and support for long contexts without requiring proprietary infrastructure. The 27B scale balances capability with practical inference requirements on available hardware. Transparency as an open model aids reproducibility and safety research.
Typical usage includes fine-tuning for domain-specific vision-language tasks and integration into chat or analysis tools. Developers deploy it for image captioning, visual QA, and document understanding workflows. It also serves educational and prototyping needs where full model access is essential.
Capabilities
How Gemma 3 27B compares
Gemma 3 27B (striped bar) vs other multimodal on intelligence, speed and price.
Price
USD per 1M output tokens · Lower is better · Gemma 3 27B ranks #7 of 139
Sources: Artificial Analysis (intelligence, speed) · OpenRouter (price).
Best for
Long-context multimodal document analysis
Handles extended technical reports or research papers containing embedded diagrams, charts, and images within its 131072-token window for comprehensive summarization and question answering.
Video frame and transcript reasoning
Processes sequences of video frames alongside spoken or textual content to generate descriptions, answer queries, or extract events across long clips.
Multimodal code and UI prototyping
Interprets screenshots, design mockups, and lengthy codebases together to suggest improvements, debug issues, or generate new interface code.
Strengths & limitations
Strengths
- +Strong efficiency at 27B scale
- +Native 128k context support
- +Solid multimodal integration
- +Open weights for customization
Limitations
- –Weaker on highly complex multi-step tasks than larger models
- –Vision performance can lag specialized vision models
- –May require careful prompting for best results
Cost calculator
Estimate what Gemma 3 27B would cost for your usage.
Based on Gemma 3 27B's $0.08/1M input · $0.16/1M output. Estimate only — actual cost varies by provider and caching.
Download & self-host Gemma 3 27B
This is an open-weight model. Download the weights from Hugging Face or load it directly with Transformers.
# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"
# Download the model weights
hf download google/gemma-3-27b-it
# Or load it directly in Python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("google/gemma-3-27b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-3-27b-it", device_map="auto")Inference providers
Hosted APIs that serve Gemma 3 27B (via Hugging Face Inference Providers).
Quick start
OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. Only the model slug changes between models.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
model: "google/gemma-3-27b-it",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);Model slug: google/gemma-3-27b-it
Editor's verdict
Gemma 3 27B is Google's open-weight multimodal with a 131K-token context window.
At $0.16 per 1M output tokens, it is very cost-efficient for its class.
As an open-weight model you can self-host it (27B parameters) or call it through a hosted API.
Best suited to strong efficiency at 27b scale and native 128k context support.
Frequently asked questions
Gemma models are released as open weights under a permissive license, allowing free local or cloud inference subject to Google's terms of use.
User reviews
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