What is Gemma 4 12B?

Google shipped Gemma 4 12B on June 3, 2026. It is an open-weights, 11.95B-parameter model that accepts text, images, audio, and video as input, returns text, and can run on a laptop with 16GB of memory. The main implementation detail: it is a mid-sized multimodal model with native audio input and no separate vision or audio encoder.

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Most multimodal models attach a vision encoder and an audio encoder to a language model. Gemma 4 12B removes those extra components and feeds raw image patches and audio waveforms into the model pathway. For developers, that means one 12B model file can handle four input types, run offline, and be used commercially under Apache 2.0.

This guide explains where Gemma 4 12B fits, what its architecture changes mean in practice, and how to think about building local multimodal workflows with it. If you want to run it immediately, see the companion guide on how to use Gemma 4 12B for free.

Gemma 4 12B at a glance

Spec	Value
Released	June 3, 2026
Parameters	11.95B dense
Inputs	Text, image, audio, video
Output	Text
Context window	256K tokens
Architecture	Encoder-free unified multimodal
License	Apache 2.0
Runs on	16GB VRAM or unified memory, about 8GB at 4-bit
Variants	google/gemma-4-12B base, google/gemma-4-12B-it instruction-tuned

What Gemma 4 12B is

Gemma 4 12B is a dense, open model from Google DeepMind. It accepts multimodal input and produces text output. The instruction-tuned variant, gemma-4-12B-it, is the version most developers will use for chat, function calling, tool use, and instruction-following workflows.

It sits in the middle of the Gemma 4 lineup. Google positions it between the smaller edge-friendly E4B model and the larger 26B Mixture-of-Experts model. The trade-off is straightforward: near-larger-model quality on hardware that many developers already own.

Where Gemma 4 12B fits in the Gemma 4 family

Gemma 4 did not launch as one single model. E2B, E4B, 26B, and 31B arrived on March 31, 2026. Gemma 4 12B was added on June 3.

Model	Size	Context	Notes
Gemma 4 E2B	2.3B effective, 5.1B raw	128K	On-device, audio input
Gemma 4 E4B	4.5B effective, 8B raw	128K	Compact, audio input
Gemma 4 12B	11.95B dense	256K	Encoder-free, audio input
Gemma 4 26B A4B	4B active, 26B total MoE	256K	Mixture-of-experts
Gemma 4 31B	31B dense	256K	Frontier performance

The 12B model is the one built around the encoder-free design. The other models use more traditional modality-specific components, such as a vision encoder and, for the smaller models, a conformer audio encoder.

For broader open-model context, see the comparison of MiniMax M3, DeepSeek V4, and Qwen 3.7 and the wider open-weight price war.

What “encoder-free” means in practice

A typical multimodal stack looks like this:

image -> vision encoder -> projector -> language model
audio -> audio encoder  -> projector -> language model
text  -------------------------------> language model

That design works, but it means you load and operate multiple components.

Gemma 4 12B simplifies the pipeline:

text tokens      \
image patches     -> unified model pathway -> text output
audio waveforms  /
video input      /

According to Google’s writeup:

• Vision input uses a lightweight embedding module: matrix multiplication, positional embeddings, and normalization.
• Audio input does not use a separate audio encoder. Raw audio is projected into the same dimensional space as text tokens.

The result is one model backbone handling all supported modalities.

Two efficiency choices matter for local deployment:

• Per-layer embeddings, or PLE: each decoder layer gets a small dedicated embedding that combines token identity with context-aware projection.
• Shared KV cache: later layers reuse key-value tensors from earlier layers, reducing memory cost during long-context inference.

Google also ships a Multi-Token Prediction drafter for speculative decoding, which can improve end-to-end inference speed by up to roughly 3x without changing output quality.

What you can build with native audio

Gemma 4 12B is useful when you want local multimodal processing without sending media to a hosted API.

Good fit use cases include:

• Local meeting transcription and summarization
• Speaker diarization workflows
• Audio question answering
• Video understanding with both frames and sound
• Screenshot or UI analysis
• Image captioning and visual reasoning
• Local coding assistants with long project context
• Agentic workflows using function calling and tool use

When mixing modalities, input order matters. The chat template expects image content before the text prompt and audio after it. The output is always text.

A practical prompt structure might look like this conceptually:

{
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "image", "source": "screenshot.png" },
        { "type": "text", "text": "Explain what is happening in this UI and summarize the audio notes." },
        { "type": "audio", "source": "notes.wav" }
      ]
    }
  ]
}

The exact request shape depends on the runner or API wrapper you use.

Published benchmark results

These are published scores for the instruction-tuned gemma-4-12B-it from the Hugging Face model card:

Benchmark	Gemma 4 12B-it
MMLU Pro, reasoning	77.2%
AIME 2026, math, no tools	77.5%
GPQA Diamond, science	78.8%
LiveCodeBench v6, coding	72.0%
Codeforces, ELO	1659
MMMU Pro, vision	69.1%
MATH-Vision	79.7%
MRCR v2, 128K, 8-needle, long context	43.4%

Compared with neighboring Gemma 4 models:

Benchmark	E4B	12B	26B A4B	31B
MMLU Pro	69.4%	77.2%	82.6%	85.2%
AIME 2026	42.5%	77.5%	88.3%	89.2%
GPQA Diamond	58.6%	78.8%	82.3%	84.3%
LiveCodeBench v6	52.0%	72.0%	77.1%	80.0%

The practical takeaway: Gemma 4 12B is much stronger than the 4B-class E4B and approaches the larger 26B model on several benchmarks, while requiring less memory.

What changed from Gemma 3

If you used Gemma 3, these are the main changes to account for:

1. Native audio input
   
   Gemma 3 handled text and vision. Gemma 4 12B adds audio and video-with-audio workflows.

2. Encoder-free multimodality
   
   You do not need to load separate vision or audio encoders.

3. 256K context window
   
   This gives more room for long documents, transcripts, codebases, and mixed media context.

4. Apache 2.0 license
   
   Gemma 4 uses the standard Apache 2.0 license, which is simpler for commercial use, modification, and redistribution.

Implementation pattern: local model plus API testing

A common local setup looks like this:

your app -> local model server -> Gemma 4 12B

For example, if your runner exposes an OpenAI-compatible local endpoint, your app may send a chat completion request similar to this:

curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma-4-12b-it",
    "messages": [
      {
        "role": "user",
        "content": "Summarize this transcript into action items."
      }
    ]
  }'

Before wiring that into an application, validate:

• endpoint URL
• model name
• request schema
• response schema
• streaming behavior, if used
• tool/function calling payloads
• error responses

A tool like Apidog can help you save the local endpoint, send sample prompts, inspect JSON responses, and document the API contract before you build against it. You can also download Apidog and test a local server directly. More setup details are in the free usage guide.

License: what Apache 2.0 allows

Gemma 4 12B is released under Apache 2.0.

In practice, that means you can:

• use it commercially
• modify and fine-tune it
• redistribute it
• run it in closed-source products
• keep your outputs

This is different from earlier Gemma releases that used a custom Gemma license. Apache 2.0 is a widely used permissive license, which usually makes review easier for commercial projects.

Hardware requirements

Google targets 16GB machines, either VRAM or unified memory.

Approximate memory targets:

Mode	Approximate memory
Full quality	Around 16GB
8-bit	Roughly 14GB
4-bit, Q4_K_M	About 8GB

That makes Gemma 4 12B realistic for:

• consumer GPUs with enough VRAM
• 16GB MacBooks with unified memory
• mid-range local workstations
• offline developer machines

If your hardware is tighter, the E2B and E4B models require less memory.

Limitations to design around

Gemma 4 12B is still a 12B open model. Build guardrails into production workflows.

Known limitations include:

• it can generate incorrect or outdated facts
• it can reflect training-data biases
• it may miss sarcasm, nuance, or figurative meaning
• common-sense reasoning is limited compared with larger frontier models
• output quality depends heavily on prompt clarity and provided context

For critical workflows, add validation steps such as:

model output -> schema validation -> human review or automated checks -> final action

For code generation, run tests before applying changes:

npm test
# or
pytest
# or
go test ./...

For factual summaries, keep source references in the prompt and verify important claims.

FAQ

Is Gemma 4 12B free?

Yes. The weights are open under Apache 2.0 and available from Hugging Face and Kaggle. You only pay for the hardware or cloud environment you run it on. See how to use Gemma 4 12B for free.

Can Gemma 4 12B understand audio?

Yes. It takes raw audio as input and can be used for transcription, speaker identification, and question answering over sound. The notable point is that it does this natively instead of relying on a separate speech model.

What is the difference between gemma-4-12B and gemma-4-12B-it?

gemma-4-12B is the base pretrained model. gemma-4-12B-it is instruction-tuned for chat, tool use, and following directions. Most application developers should start with gemma-4-12B-it.

How is 12B different from 26B and 31B?

Gemma 4 12B is dense, encoder-free, and designed for 16GB-class machines. The 26B model is Mixture-of-Experts, with 4B active and 26B total parameters. The 31B model is a larger dense model focused on higher benchmark performance. The larger models score higher but need more memory.

Does Gemma 4 12B support tool calling?

Yes. It supports text and multimodal function calling, along with an optional thinking mode for step-by-step reasoning. That makes it suitable for local agentic workflows.

How does it compare to Gemini 3.5?

They target different use cases. Gemini 3.5 is Google’s hosted frontier model; see what is Gemini 3.5. Gemma 4 12B is an open model you run yourself. You trade some peak model quality for offline use, privacy, and no per-token inference cost on your own hardware.