Developers building on-device or self-hosted multimodal agents get a model that fits about 6.6GB of VRAM and skips per-token API bills entirely. It replaces separate vision-plus-audio encoder pipelines with one encoder-free model, though teams chasing top-tier agentic coding should size up to Gemma 4 31B instead.
Gemma 4 12B scores 78.8% on GPQA Diamond, Google DeepMind's mid-sized open-weight multimodal model released June 3, 2026. Its encoder-free architecture reads text, images, audio and video directly, running on a single consumer GPU under an Apache 2.0 license with no per-token fee.
Where it sits
- --$ per 1M tokensBlended price (3:1)Lower is better-- / 64peer median $1.70/Mvendor price, checked by HokAI
- --tokens/sOutput speedHigher is better-- / 39peer median 90 tok/scited: Artificial Analysis
- --% solvedSWE-bench VerifiedHigher is better-- / 28peer median 78.3%per source, see benchmark scores
- 78.8%% correctGPQA DiamondHigher is better#29 / 44peer median 88.3%per source, see benchmark scores
Mid-pack on GPQA Diamond (rank 29 of 44), and one of 65 whose vendor states it does not train on customer data.
Ranks are against GA models on HokAI that publish the same figure; ties share a rank.
Provider: Google DeepMind · Family: Gemma 4
More about Google DeepMind on HokAI
Context window: 262,144 tokens
Input modalities: text, image, audio, video · Output: text, tool-calls
About Gemma 4 12B
Gemma 4 12B is an open-weight multimodal language model built by Google DeepMind, released June 3, 2026 as the mid-sized addition to the Gemma 4 family that launched April 2, 2026 alongside the E2B, E4B, 26B-A4B mixture-of-experts, and 31B dense variants. It sits between the tiny edge-focused E4B and the larger 26B/31B models, aimed at developers who need a single laptop-runnable model that reads text, images, audio, and video without shipping four separate pipelines. It uses a dense, decoder-only Transformer built on the same backbone as Gemma 4 31B Dense, with roughly 12 billion parameters.
The defining architectural choice is going encoder-free. Earlier multimodal stacks bolt on a heavyweight vision encoder (often 500M+ parameters) and a separate audio encoder, then fuse their embeddings into the language model. Gemma 4 12B drops both: a 35M-parameter linear vision embedder projects raw 48x48 image patches directly into the model's hidden dimension in a single matrix multiplication, with spatial position added through factorized X/Y coordinate lookup tables instead of a full attention stack. Audio gets the same lightweight-projection treatment, which is why Google reports lower multimodal inference latency than the encoder-heavy designs used in Gemma 3 and most competing open models.
On benchmarks, independent write-ups (Techsy, BuildFastWithAI, AICybr) report Gemma 4 12B scoring roughly 78.8% on GPQA Diamond and 94.9% on DocVQA. Google has not published an official SWE-bench or MMLU-Pro score for the 12B variant specifically; those numbers exist for the larger 31B sibling, which posts 89.2% on AIME 2026 and 80.0% on LiveCodeBench v6, so treat the 12B reasoning figures as third-party reported rather than vendor-confirmed until Google ships a dedicated model card update.
Context window matches the rest of the Gemma 4 family, which lets a 12B-class model handle full codebases, long PDFs, or hour-plus video transcripts without chunking. Google has not published a separate max-output-token ceiling distinct from that shared context budget.
Modality support is the headline feature: text, image, video (processed as sampled frame sequences), and audio input, with the E2B, E4B, and 12B checkpoints all sharing audio support (the larger 26B and 31B variants do not, per Google's own comparison). Capabilities include object detection, document and PDF parsing, screen and UI understanding, chart reading, multilingual OCR, and handwriting recognition. Interleaved multimodal prompts, mixing text and images in any order, are supported natively rather than through a special API mode.
Deployment options span Hugging Face, Kaggle, Ollama, LM Studio, vLLM, SGLang, MLX, and llama.cpp for local or self-managed serving, plus Google Cloud's Vertex AI Model Garden, Cloud Run GPU, and GKE for managed cloud deployment; AWS added Gemma 4 support to Bedrock in mid-June 2026. It ships free to self-host under an open license, with third-party hosted-API rates covered under pricing. Google also ships an official multi-token-prediction (MTP) drafter model for Gemma 4 that adds speculative decoding, claiming up to 3x faster generation with no quality loss when paired with the 12B base model.
The model shipped under a plain Apache 2.0 license, a meaningful change from the earlier custom "Gemma Terms of Use" that governed Gemma 1 through 3. That older license was broadly permissive but carried Google-specific use restrictions that made some enterprise legal teams hesitate; Apache 2.0 is the same OSI-approved license used by Qwen and Mistral, meaning commercial fine-tuning, redistribution, and paid hosting are unambiguously allowed with just attribution and license-text inclusion, no derivative-weight sharing required.
Pre-training data has a cutoff of January 2025 and spans web documents, code, images, and audio across more than 140 languages, filtered for personal information, CSAM, and general safety and quality issues per Google's stated data-handling policy. Best fit is developers building on-device or self-hosted multimodal agents, such as browser automation, document pipelines, or voice-plus-vision assistants, who want to keep data on their own infrastructure.
Pricing
Gemma 4 12B is free to self-host under an open license: there is no per-token fee and no rate limit when you run it on your own GPU. Google does not sell hosted inference for Gemma directly. Third-party host SiliconFlow prices its managed API for the model at $0.10 for every 1M input tokens and $0.30 for every 1M output tokens, roughly $0.12 blended, as of July 2026.
Key Features
- Encoder-free multimodal input: A 35M-parameter linear embedder projects raw image patches and audio directly into the model, skipping the heavyweight vision/audio encoders most multimodal models rely on.
- 256K token context window: 262,144 tokens shared across the whole Gemma 4 family, enough for full codebases or long video transcripts on a 12B-class model.
- Runs on 8GB consumer GPUs: About 6.6GB VRAM at Q4_K_M quantization, with official Google QAT checkpoints that hold quality close to bf16.
- Native function calling: Structured tool-use support built in for agentic workflows, without a separate fine-tune or adapter.
- Video and audio understanding: Processes video as sampled frame sequences and accepts native audio input, alongside text and image, in one unified model.
Pros
- Carries no per-token license fee and drops the Google-specific usage restrictions that came with the older Gemma Terms of Use.
- Encoder-free architecture that Google says cuts multimodal inference latency compared with encoder-heavy rivals of similar size.
- Fits on an 8GB GPU or a 16GB unified-memory laptop thanks to official quantization-aware training checkpoints.
Cons
- No official SWE-bench, AIME or MMLU-Pro score published for the 12B; only third-party GPQA Diamond and DocVQA figures exist so far.
- Audio input only, no audio output, so voice assistants still need a separate TTS model.
- Meaningfully behind its own 31B sibling and closed frontier models like Gemini 3.1 Pro on ceiling reasoning tasks.
Benchmarks
- Docvqa: 94.9 vendor-reported · 03 Jun 2026
- GPQA Diamond: 78.8% vendor-reported · 03 Jun 2026 — PhD-level science questions that are hard to search for, % correct.
A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.
Frequently Asked Questions
How much does Gemma 4 12B cost in 2026?
Nothing, if you self-host Gemma 4 12B on your own GPU: it's open-weight with no license fee. SiliconFlow's managed endpoint bills input tokens at $0.10 per million and output tokens at $0.30 per million, about $0.12 blended, which works out to roughly $0.16 for a 1M-input, 200K-output run. Google itself does not sell Gemma inference directly.
What do you get on Gemma 4 12B's free tier?
The whole model, with no tier at all: self-hosting Gemma 4 12B carries no license fee, no seat limits, and no expiring trial since it runs on your own hardware. It needs roughly enough VRAM to hold a heavily quantized 12B-parameter model, an 8GB consumer GPU or a similarly modest Apple Silicon Mac is generally enough. The only ongoing cost is your own electricity, not a Google or third-party bill.
What are the best alternatives to Gemma 4 12B?
The closest competitors are Gemma 3 12B, Llama 4 Scout, and Qwen3 14B, similarly sized open-weight models with multimodal or agentic strengths of their own. Choose Llama 4 Scout if you specifically need Meta's tool ecosystem, or Qwen3 14B if multilingual coverage matters more than encoder-free latency. Gemma 4 12B's edge is combining text, image, audio, and video input in one checkpoint without a separate vision or audio encoder.
Is Gemma 4 12B better than Gemma 4 31B?
Not on reasoning: Gemma 4 31B is the family's reasoning-focused sibling, and Google's own benchmarks put it at 89.2% on AIME 2026, a tier the 12B has no vendor-confirmed equivalent for yet. Both share the same context window and encoder-free multimodal input, but the 31B needs data-center-class hardware while the 12B fits an 8GB consumer GPU. Pick the 31B for agentic coding and math-heavy work; pick the 12B for a laptop-runnable multimodal agent.
What does it take to start using Gemma 4 12B?
Pull the official google/gemma-4-12B-it-qat-q4_0-gguf checkpoint from Hugging Face rather than an older community re-quant, since early community conversions shipped a chat template that broke function-calling formatting. Load it in Ollama, LM Studio, or llama.cpp; on an 8GB GPU it runs at interactive speed with no extra configuration. For managed hosting instead of local setup, Google Vertex AI Model Garden and AWS Bedrock both list Gemma 4 12B, and SiliconFlow offers pay-per-token API access without any GPU at all.
Top Alternatives
- Gemma 4 31B: Pick Gemma 4 31B if you need its higher reasoning benchmarks and can run it on data-center hardware; pick Gemma 4 12B to fit an 8GB GPU.
- Gemini 3.1 Pro: Pick Gemini 3.1 Pro for a higher benchmark ceiling and native audio output; pick Gemma 4 12B for free self-hosting on your own hardware.
HokAI guides covering Gemma 4 12B
- Best AI Models You Can Run Locally for Coding in 2026: Qwen3.8-27B beat Gemma 4 31B 12 to 6 on real coding tasks in a 24GB-GPU test. See which open-weight models actually fit your GPU, license and RAM in 2026.