Gemma 4 31B
Gemma 4 31B is an open-weight model release in the Gemma family from Google DeepMind14, version 4 (31B), released Mar 31, 2026. Its weights can be downloaded by the public123 under the Apache License 2.051.
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)
On AI on Hugging Face: google/gemma-4-31B-it, google/gemma-4-31B
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.
Component reuse rights
- weights
- Reviewed qualifying license recorded — check scope and conditions
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
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 informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness. | Unknown | Not assessed. |
| Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable. | Unknown | Not assessed. |
| Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient. | Unknown | Not assessed. |
| Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above. | 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
- EmbeddingGemma 2Model-disclosure tier (USASI rubric v0.2): Open-weight
- EmbeddingGemma 300MModel-disclosure tier (USASI rubric v0.2): Open-weight
- Gemma 4 12B UnifiedModel-disclosure tier (USASI rubric v0.2): Open-weight
- Gemma 4 26B A4BModel-disclosure tier (USASI rubric v0.2): Open-weight
What this catalog does not know
- Training code: unknown.
- Training-data information: unknown.
- Training-data access: unknown.
- Complete training pipeline: unknown.
Have a primary source? How to report a correction.
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.
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APA style
USASI. (2026, October 2). Gemma 4 31B. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/gemma-4-31b/
BibTeX
@misc{usasi_gemma_4_31b,
author = {{USASI}},
title = {Gemma 4 31B},
year = {2026},
month = oct,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/gemma-4-31b/}},
note = {United States of America Superintelligence. Last updated 2026-10-02}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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