AI Model APIs

Gemma 4

Name: Gemma 4
Brand: Gemma 4
Availability: InStock

Gemma 4 is a Google DeepMind AI model in the Gemma family, designed for building and running generative AI applications.

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Overview

Gemma 4 is an open-weights AI model family from Google DeepMind, purpose-built for advanced reasoning and agentic workflows, available free under Google's Gemma open license. It targets developers, researchers, and enterprises that want to fine-tune, self-host, or embed large language models in production applications without the per-token API costs of closed frontier models.

As the next generation in the Gemma lineup—following Gemma (2024), Gemma 2 (June 2024, offering 2B, 9B, and 27B variants), and Gemma 3 (March 2025, offering 1B, 4B, 12B, and 27B variants)—Gemma 4 inherits the architectural lineage of Google's Gemini frontier models but ships with publicly downloadable weights so teams can run it on their own GPUs, on-device, or via cloud providers like Vertex AI, Hugging Face, Kaggle, and Ollama. Google DeepMind positions Gemma 4 around two core capabilities: stronger chain-of-thought reasoning and tool-use for agent pipelines (function calling, retrieval, multi-step planning).

Gemma 4 sits in a competitive slice of the market: open-weights models from a major frontier lab. Compared to closed APIs like GPT-4o ($2.50–$10 per 1M tokens) or Claude, Gemma 4 offers total deployment control, data residency, and zero per-token cost at inference. Compared to other open models like Meta's Llama 4, Mistral, Qwen, and DeepSeek, Gemma 4 differentiates on tight integration with the Google AI stack (Vertex AI, Keras, JAX, TensorFlow, AI Studio) and Google's responsibility tooling. Teams already running on Google Cloud, or those needing a permissively licensed model for commercial fine-tuning, are the natural fit.

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Key Features

Open weights with permissive commercial license+

Gemma 4 ships with downloadable weights under the Gemma license, which allows commercial deployment, fine-tuning, and redistribution of derivatives. This makes it suitable for SaaS products, internal enterprise tools, and on-prem installations where closed APIs are not an option.

Advanced reasoning and chain-of-thought+

Google DeepMind explicitly positions Gemma 4 as purpose-built for advanced reasoning, building on the research lineage that produced Gemini's thinking modes. This makes it a stronger fit for math, code, and multi-step problem-solving than typical small open models, narrowing the gap with closed frontier APIs.

Agentic workflow support+

The model family is tuned for tool use, function calling, and structured outputs that agent harnesses rely on. Teams can wire Gemma 4 into LangChain, LlamaIndex, or custom orchestrators and get reliable JSON-shaped responses, making it usable as the reasoning core of an autonomous agent.

Multi-size model family+

Following the pattern of Gemma 3 (1B, 4B, 12B, 27B parameters), the Gemma 4 family offers multiple parameter sizes so teams can match the model to their compute budget. Smaller variants run on a single consumer GPU or even on-device after quantization, while larger variants target serious server hardware for higher-quality output.

Deep integration with the Google AI stack+

Gemma 4 is supported across Vertex AI Model Garden, Google AI Studio, Kaggle, JAX, Keras, and TensorFlow, in addition to Hugging Face Transformers, PyTorch, and Ollama. This first-class tooling cuts integration time and gives teams managed deployment options on Google Cloud without losing the freedom to self-host elsewhere.

Pricing Plans

Open Weights

✓Free download of all Gemma 4 model variants
✓Commercial use permitted under the Gemma license
✓Fine-tuning and redistribution of derivatives allowed
✓Available on Kaggle, Hugging Face, Vertex AI Model Garden, and Ollama
✓Reference inference and fine-tuning code provided

Vertex AI Hosted

From ~$0.70/hr (NVIDIA L4) to ~$8.98/hr (H100 80 GB) per GPU on Google Cloud on-demand pricing

✓Managed deployment in Vertex AI Model Garden with one-click endpoints
✓Auto-scaling inference endpoints with per-second billing
✓Reference GPU costs: NVIDIA L4 ~$0.70/hr, A100 40 GB ~$2.21/hr, A100 80 GB ~$3.67/hr, H100 80 GB ~$8.98/hr (us-central1 on-demand)
✓Enterprise IAM, VPC, and audit logging included
✓Integration with Vertex AI Pipelines and Agent Builder

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Best Use Cases

🎯

Fine-tuning a domain-specific assistant on proprietary data that cannot leave a company's network, such as healthcare, legal, or financial workflows where data residency rules out closed APIs

⚡

Building agentic pipelines with tool use and function calling where per-token API costs would be prohibitive at scale, such as background batch processing or high-volume customer support automation

🔧

Running on-device or edge inference for mobile apps, desktop assistants, and offline scenarios using small quantized Gemma 4 variants via Ollama or MLC

🚀

Powering retrieval-augmented generation (RAG) services on internal knowledge bases where teams want full control over the model and embedding stack

💡

Academic and applied research that requires reproducible weights, the ability to inspect or modify the model, and freedom to publish derivative checkpoints

🔄

Replacing or complementing a closed API in a hybrid setup - routing common queries to self-hosted Gemma 4 and escalating only the hardest cases to Gemini or other frontier APIs to cut spend

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Gemma 4 doesn't handle well:

⚠Open-weights models from any lab generally lag behind the largest closed frontier models on the most challenging multi-step reasoning and very long context tasks
⚠Running Gemma 4 in production requires managing GPU capacity, autoscaling, observability, and updates - infrastructure that managed APIs handle for you
⚠The Gemma license has prohibited-use restrictions and update terms, so legal review is recommended before shipping in regulated industries
⚠Multimodal coverage (vision, audio, video) is typically narrower in the Gemma family than in flagship Gemini models
⚠Smaller third-party fine-tune ecosystem compared to Llama, meaning fewer pre-trained domain checkpoints to drop in

Pros & Cons

✓ Pros

✓Free to download and run with no per-token inference costs, unlike closed API models that charge $2.50–$15 per million tokens
✓Permissive Gemma license permits commercial use, redistribution of fine-tunes, and on-prem deployment for regulated industries
✓Backed by Google DeepMind, the same lab behind Gemini, AlphaFold, and AlphaGo, giving stronger research provenance than most open-model releases
✓Prior Gemma generations offered 4 parameter sizes (e.g., Gemma 3: 1B, 4B, 12B, 27B), letting teams match the model to their hardware from on-device to multi-GPU
✓First-class support across Vertex AI, Hugging Face, Kaggle, Ollama, and major frameworks (JAX, PyTorch, Keras), reducing MLOps integration time
✓Purpose-built for agentic workflows with tool use and reasoning, narrowing the gap between open models and closed frontier APIs

✗ Cons

✗Self-hosting requires GPU infrastructure and MLOps expertise that smaller teams may lack
✗Open-weights models from any lab, including Google, have historically scored below the largest closed frontier models on the hardest reasoning benchmarks
✗Use is bound by the Gemma license terms, which include prohibited-use restrictions and are not OSI-approved open source
✗Limited multimodal capabilities compared to Google's flagship Gemini models that handle native video, audio, and long-context vision
✗Community ecosystem and third-party fine-tunes are smaller than Llama's, so off-the-shelf checkpoints for niche tasks may be scarcer

Frequently Asked Questions

Is Gemma 4 actually free to use commercially?+

Yes, Gemma 4 is released under the Gemma license, which permits commercial use, fine-tuning, and redistribution of derivative models. There is no per-token inference fee because you run the model on your own infrastructure or via a cloud provider's compute pricing. However, the license is not OSI-certified open source - it includes a prohibited-use policy covering things like generating CSAM, harassment, and certain regulated decisions. Most standard SaaS, enterprise, and research use cases are explicitly allowed.

How does Gemma 4 compare to Gemini?+

Gemini is Google's closed, hosted frontier model family accessed through API and consumer apps; Gemma 4 is the open-weights sibling you can download and run yourself. Gemini Ultra-class models will generally outperform Gemma 4 on the hardest reasoning, long-context, and multimodal tasks because they are larger and use proprietary infrastructure. Gemma 4, however, gives you full deployment control, fixed compute costs, on-device options, and the ability to fine-tune freely. Many teams use both: Gemini for hardest queries and Gemma for high-volume, latency-sensitive, or data-sensitive paths.

What hardware do I need to run Gemma 4?+

Hardware requirements depend on the variant and quantization level. As a reference from prior Gemma generations: Gemma 3 1B ran on CPUs and phones, the 4B variant fit on a single consumer GPU (8 GB+ VRAM), the 12B needed roughly 16 GB VRAM, and the 27B required an A100 or equivalent (40–80 GB) at full precision or a 24 GB GPU with 4-bit quantization. Gemma 4 variants will have their own specific requirements listed on the model cards at release. Quantized GGUF builds via Ollama or llama.cpp typically cut memory needs by 2–4x. For production traffic, most teams deploy on Vertex AI, AWS, or Hugging Face Inference Endpoints rather than self-managing GPUs.

Where can I download Gemma 4?+

Gemma models are distributed through Kaggle, Hugging Face, Vertex AI Model Garden, and Google AI Studio, with Ollama and llama.cpp typically picking up community quantizations shortly after release. You will be asked to accept the Gemma license terms before downloading. The official source of truth is the Gemma page on deepmind.google, which links out to the supported distribution channels and provides reference code for inference and fine-tuning.

Is Gemma 4 a good choice for building AI agents?+

Google DeepMind has explicitly positioned Gemma 4 around advanced reasoning and agentic workflows, meaning it is trained and tuned to handle multi-step planning, tool calling, and structured outputs that agents depend on. For production agents, it is a strong open option, especially when you need predictable latency, on-prem deployment, or fine-tuning on private tool schemas. Compared to closed APIs like GPT-4 or Claude with mature function-calling, you may need to do more prompt and harness engineering yourself, but you avoid per-call costs and vendor lock-in.

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What's New in 2026

Gemma 4 is positioned by Google DeepMind as the next generation of the Gemma open model family, following Gemma 3 (March 2025, with 1B/4B/12B/27B parameter variants). The headline shift is toward agent-grade capabilities (tool use, multi-step planning) versus prior Gemma generations. Check the official model page and Hugging Face model cards for confirmed variant sizes, benchmark results, and supported distribution channels as they are published.

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User Reviews

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More about Gemma 4

Pricing Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Overview

Key Features

Open weights with permissive commercial license+

Advanced reasoning and chain-of-thought+

Agentic workflow support+

Multi-size model family+

Deep integration with the Google AI stack+

Pricing Plans

Open Weights

✓Free download of all Gemma 4 model variants
✓Commercial use permitted under the Gemma license
✓Fine-tuning and redistribution of derivatives allowed
✓Available on Kaggle, Hugging Face, Vertex AI Model Garden, and Ollama
✓Reference inference and fine-tuning code provided

Vertex AI Hosted

From ~$0.70/hr (NVIDIA L4) to ~$8.98/hr (H100 80 GB) per GPU on Google Cloud on-demand pricing

✓Managed deployment in Vertex AI Model Garden with one-click endpoints
✓Auto-scaling inference endpoints with per-second billing
✓Reference GPU costs: NVIDIA L4 ~$0.70/hr, A100 40 GB ~$2.21/hr, A100 80 GB ~$3.67/hr, H100 80 GB ~$8.98/hr (us-central1 on-demand)
✓Enterprise IAM, VPC, and audit logging included
✓Integration with Vertex AI Pipelines and Agent Builder

Best Use Cases

🎯

Fine-tuning a domain-specific assistant on proprietary data that cannot leave a company's network, such as healthcare, legal, or financial workflows where data residency rules out closed APIs

⚡

Building agentic pipelines with tool use and function calling where per-token API costs would be prohibitive at scale, such as background batch processing or high-volume customer support automation

🔧

Running on-device or edge inference for mobile apps, desktop assistants, and offline scenarios using small quantized Gemma 4 variants via Ollama or MLC

🚀

Powering retrieval-augmented generation (RAG) services on internal knowledge bases where teams want full control over the model and embedding stack

💡

Academic and applied research that requires reproducible weights, the ability to inspect or modify the model, and freedom to publish derivative checkpoints

🔄

Replacing or complementing a closed API in a hybrid setup - routing common queries to self-hosted Gemma 4 and escalating only the hardest cases to Gemini or other frontier APIs to cut spend

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Gemma 4 doesn't handle well:

⚠Open-weights models from any lab generally lag behind the largest closed frontier models on the most challenging multi-step reasoning and very long context tasks

⚠Running Gemma 4 in production requires managing GPU capacity, autoscaling, observability, and updates - infrastructure that managed APIs handle for you

⚠The Gemma license has prohibited-use restrictions and update terms, so legal review is recommended before shipping in regulated industries

⚠Multimodal coverage (vision, audio, video) is typically narrower in the Gemma family than in flagship Gemini models

⚠Smaller third-party fine-tune ecosystem compared to Llama, meaning fewer pre-trained domain checkpoints to drop in

Pros & Cons

✓ Pros

✓Free to download and run with no per-token inference costs, unlike closed API models that charge $2.50–$15 per million tokens
✓Permissive Gemma license permits commercial use, redistribution of fine-tunes, and on-prem deployment for regulated industries
✓Backed by Google DeepMind, the same lab behind Gemini, AlphaFold, and AlphaGo, giving stronger research provenance than most open-model releases
✓Prior Gemma generations offered 4 parameter sizes (e.g., Gemma 3: 1B, 4B, 12B, 27B), letting teams match the model to their hardware from on-device to multi-GPU
✓First-class support across Vertex AI, Hugging Face, Kaggle, Ollama, and major frameworks (JAX, PyTorch, Keras), reducing MLOps integration time
✓Purpose-built for agentic workflows with tool use and reasoning, narrowing the gap between open models and closed frontier APIs

✗ Cons

✗Self-hosting requires GPU infrastructure and MLOps expertise that smaller teams may lack
✗Open-weights models from any lab, including Google, have historically scored below the largest closed frontier models on the hardest reasoning benchmarks
✗Use is bound by the Gemma license terms, which include prohibited-use restrictions and are not OSI-approved open source
✗Limited multimodal capabilities compared to Google's flagship Gemini models that handle native video, audio, and long-context vision
✗Community ecosystem and third-party fine-tunes are smaller than Llama's, so off-the-shelf checkpoints for niche tasks may be scarcer