Gemma 4 31B
Release in the Gemma family · version 4 (31B)
Maintained by Google DeepMind14
Gemma 4 31B is the dense model in the Gemma 4 generation, with 30.7 billion parameters. It accepts text and image input, can process video as frames, and generates text, with a 256K-token context window. It is published as pre-trained and instruction-tuned checkpoints; Google's release page dates the initial Gemma 4 release, which included this size, to March 31, 2026.149
- Model hub: Hugging Face (instruction-tuned) (external site: huggingface.co)
- Model hub: Hugging Face (pre-trained) (external site: huggingface.co)
- Documentation: Gemma 4 model card (external site: ai.google.dev)
- License: Gemma 4 license (external site: ai.google.dev)
- Paper: Gemma 4 Technical Report (external site: arxiv.org)
Availability and license
Overall availability
Weights are downloadable from Hugging Face; the repositories were not gated at the time of review. Use is governed by the Apache License 2.0.1238
Availability is separate from permission: read the license before using or redistributing.
Model-disclosure tier
The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| WeightsCan the general public download the model parameters for this release? | Public | Pre-trained and instruction-tuned checkpoints are published in safetensors format on Hugging Face.123 |
| Inference codeIs code for running the model published? | Public | The model card documents inference with Hugging Face Transformers; Google DeepMind's Apache-2.0 gemma JAX library also supports Gemma 4.110 |
| Training codeIs the code used to train the model published? | Unknown | The gemma JAX library includes fine-tuning code. This catalog did not find published code used to pre-train Gemma 4.10 |
| Training-data informationPublic = the training data itself can be obtained. Partial = composition or sources are documented without full access. | Partial | The model card and technical report describe the data types (web documents, code, mathematics, images) and a January 2025 cutoff; the data itself is not released.17 |
| Training recipeAre the training configuration and procedure documented in enough detail to follow? | Partial | The technical report describes the architecture, compute setup (TPUv4 and TPUv6e, JAX, Pathways), and data filtering, and says pre-training and post-training follow the Gemma 3 approach; it is not a complete recipe.7 |
| Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only. | Partial | Benchmark results for the instruction-tuned models are reported in the model card and technical report.17 |
What it is useful for
The model card lists text generation, chatbots, summarization, image data extraction, NLP and vision-language research, and language-learning tools among intended uses, and documents a configurable thinking mode and native function calling.1
Run and use notes
- The model card shows loading the model with Hugging Face Transformers (AutoModelForMultimodalLM) and recommends sampling with temperature 1.0, top_p 0.95, and top_k 64.1
Organization context
Other releases in the Gemma family
- Gemma 4 12B UnifiedModel-disclosure tier (USASI rubric v0.1): Open-weight
- Gemma 4 26B A4BModel-disclosure tier (USASI rubric v0.1): Open-weight
U.S. eligibility
Eligible · basis: Documented U.S. control
The model card names Google DeepMind as the author. Google DeepMind is a research unit of Google, announced by Google's CEO in 2023, and Google's parent Alphabet Inc. has its principal executive offices in Mountain View, California, per its fiscal 2025 Form 10-K.11112
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.