Gemma 3 27B
Google’s 27B-parameter open-weight transformer with multilingual support, long-context architecture, and vision-language design.
Overview
What is Gemma 3 27B
Gemma 3 27B is Google’s largest open-weight model in the Gemma 3 family, released in March 2025. It is a 27-billion-parameter multimodal transformer supporting text and image inputs with a 128K token context window, designed for reasoning, coding, and multilingual tasks on consumer-grade hardware.
Running it privately on Venice
On Venice, Gemma 3 27B runs inside a TEE with end-to-end encryption and zero retention — your prompts are never stored or used for training. You get the same open weights with full data sovereignty, plus native web search integration, without Big-Tech surveillance.
Assessment
Strengths and limitations
- Open weights released under the Gemma license, enabling local deployment and fine-tuning without proprietary lock-in.
- Architecture supports up to 128K tokens and 140+ languages, excelling at long-document and multilingual tasks.
- Efficient local-global attention design reduces KV-cache memory, making 27B-scale inference viable on consumer hardware.
- Integrated web search on Venice lets the model retrieve current information to ground responses without external tooling.
- Post-training distillation and reinforcement learning improve math, reasoning, and chat performance over Gemma 2.
- Not fully open source: the Gemma license imposes usage restrictions that fall short of OSI standards.
- On Venice the context is limited to 40K tokens, well below the model’s native 128K window.
- Coding and agentic benchmarks lag behind larger closed frontier models and top-tier open rivals.
- Multimodal vision inputs are not exposed on Venice; only text plus web search is available.
- The model is not uncensored and may refuse or moderate responses on sensitive topics.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Google DeepMind
- Released
- March 10, 2025
- Architecture
- Decoder-only transformer with local + global attention
- Parameters
- 27 billion
- Modality
- Text and image input, text output
- Open weights
- Yes — Gemma license
- Context window
- 40K tokens
- Max output
- 4.096K tokens
- Capabilities
- Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Mar 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "e2ee-gemma-3-27b-p",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.14 per 1M input tokens and $0.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Context | Open weights | Price (Venice) | Best for |
|---|---|---|---|---|
| Gemma 3 27B | 40K tokens | Yes | $0.14 in · $0.50 out / 1M | Multilingual text & web search |
| Google Gemma 4 31B Instruct | 256K tokens | Yes | $0.12 in · $0.36 out / 1M | Long-context reasoning |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 in · $0.48 out / 1M | Coding & MoE efficiency |
| Claude Sonnet 4.6 | 1M tokens | No | $3.60 in · $18 out / 1M | Agentic workflows |
| Kimi K2.6 | 256K tokens | Yes | $0.75 in · $3.50 out / 1M | Long-document analysis |
The lightweight open-weight workhorse with native web search and strong multilingual support.
Use cases
What it is good for
- 01Multilingual content generation, translation, and summarization across 140+ languages.
- 02Long-document analysis and question answering where the full context matters.
- 03Low-cost, high-volume text generation for applications that need open-weight sovereignty.
- 04Web-grounded research and fact-checking via Venice’s native search integration.
- 05Fine-tuning and local deployment on consumer GPUs thanks to efficient architecture.
Prompting
Getting better results
Use the language you need directly — Gemma 3 was trained on 140+ languages and often performs best with native prompts rather than English translation.
For long documents, place the instruction at the end of the prompt to leverage the model’s long-context attention.
Keep prompts structured and explicit; the instruction-tuned variant responds well to clear formatting like bullet points or XML tags.
Enable web search on Venice when you need current facts beyond the model’s training cutoff.
Version history
Current release with multimodal architecture and 128K context.
FAQ
Frequently asked questions
Gemma 3 27B is Google’s largest open-weight model in the Gemma 3 family, released in March 2025. It is a 27-billion-parameter transformer with native multimodal architecture, multilingual support across 140+ languages, and a 128K token context window, designed for reasoning, coding, and long-document tasks.
Venice bills per token at $0.14 per 1M input tokens and $0.50 per 1M output tokens. There is no subscription required; you only pay for what you use.
It has open weights under the Gemma license, so you can download and fine-tune it locally. However, the Gemma license is not a fully OSI-approved open-source license and carries some usage restrictions.
Yes. On Venice, Gemma 3 27B includes native web search integration, letting it retrieve current information to ground its responses.
Choose Gemma 3 27B for low-cost, high-volume multilingual tasks and web search. Choose Gemma 4 31B if you need a larger 256K context window, lower per-token pricing, and newer reasoning performance.
No. Gemma 3 27B is not uncensored and may refuse or moderate responses on certain sensitive topics.
Conversations are private with zero retention. The model runs inside a TEE with end-to-end encryption, meaning your prompts are not stored, profiled, or used for training.
Venice supports a 40K token context window and up to 4,096 tokens of output per request for this model.
Run Gemma 3 27B privately
No prompt logging. No data used for training.