TL;DR: Gemma 4 wins on efficiency, on-device deployment, and licencing simplicity. Llama 4 wins on raw capability at the largest sizes and has a more established developer ecosystem. If you're deploying to phones or edge hardware, Gemma 4. If you need maximum reasoning power on a server, Llama 4 Maverick. For most developers, the answer depends on where the model needs to run.
The open-source AI landscape in 2026 has, for the first time, produced a genuinely difficult choice. Not difficult in the "they're all mediocre in different ways" sense that characterised earlier years, but difficult because two models — Google's Gemma 4 and Meta's Llama 4 — are both good enough that picking the wrong one for your use case is a real missed opportunity.
A year ago, the default recommendation for anyone self-hosting an open-source model was some variant of "just use Llama." Meta had built enough momentum, enough community tooling, and enough benchmark credibility that the decision was easy. That changed in April 2026, when Google released Gemma 4 under an Apache 2.0 licence with a family of models engineered specifically for efficient deployment across hardware tiers.
This is a practical comparison. Not benchmarks in isolation — those are useful but insufficient — but a look at what each model actually does well, where each falls short, and which one you should choose based on what you're building. For a deep dive into everything Gemma 4 offers, see our complete Gemma 4 guide.
The Models at a Glance
Both Gemma 4 and Llama 4 are families, not single models. Understanding the lineup matters because comparing the wrong tiers against each other produces misleading conclusions.
Gemma 4 ships in three sizes. The E2B is 2.3 billion effective parameters — the smallest, designed for phones and embedded hardware. The E4B is 4.5 billion parameters, the sweet spot for laptop and edge deployment. The 27B is the flagship, a 26-billion parameter mixture-of-experts model with 3.8 billion parameters active during any single inference. All three support a 128,000 token context window, multimodal input (text, images, and audio on the smaller models), and 140-plus languages.
Llama 4 ships in two main variants that are publicly available. Scout is the smaller option — a 17-billion active parameter mixture-of-experts model with 16 experts, running 109 billion total parameters. It supports a 10-million token context window, which is an order of magnitude larger than anything else in the open-source space. Maverick is the larger option — 17 billion active parameters but with 128 experts across 400 billion total parameters. It's designed for server deployment and competes directly with the best proprietary models on reasoning benchmarks.
The most honest summary: Gemma 4's range extends further down the hardware spectrum (phones, Pi, laptops), while Llama 4's range extends further up the capability spectrum (Maverick on server hardware).
Performance: What the Benchmarks Say and What They Don't
Benchmark comparisons between these models have been published extensively since both launched, and the results tell a consistent but incomplete story.
On reasoning and knowledge benchmarks — MMLU, ARC, HellaSwag, and similar — Llama 4 Maverick scores higher than Gemma 4 27B on most tests. The margins vary by benchmark, but Maverick's advantage on pure reasoning capability is real and consistent. This is expected: Maverick is a significantly larger model with more total parameters, more experts, and was trained on what Meta describes as an unprecedented dataset.
Gemma 4 27B holds its own better than its parameter count would suggest, largely due to the mixture-of-experts architecture keeping active compute efficient. On tasks where speed matters alongside quality — real-time applications, interactive use — Gemma 4's faster inference often makes it the more practical choice even when Maverick would produce a marginally better answer given unlimited time.
At the smaller tiers, the comparison becomes lopsided in Gemma 4's favour. Llama 4 Scout, while capable, is not designed for the kind of constrained hardware that Gemma 4 E2B and E4B target. There is no Llama 4 model you can run on a phone. There is no Llama 4 model that runs comfortably on a Raspberry Pi. Gemma 4 occupies this space essentially alone among top-tier open-source releases.
On coding benchmarks — HumanEval, MBPP, and the newer SWE-bench variants — Llama 4 Maverick has a clear edge for complex, multi-step coding tasks. Gemma 4 27B is competitive for straightforward code generation, function writing, and code explanation. For developers building coding assistants or code-completion tools, the choice depends on deployment constraints: if it needs to run locally, Gemma 4 is often the only practical option; if it runs on a server, Maverick is hard to beat.
The multimodal comparison is also worth noting. Gemma 4's smaller models (E2B and E4B) accept images and audio alongside text — a capability that runs entirely on-device. Llama 4 Scout and Maverick both support images, and Maverick's image understanding is strong. But the ability to process images on a phone, offline, without any server, is something only Gemma 4 currently offers in the open-source space.
What the benchmarks don't capture well: real-world performance on your specific tasks. Benchmarks test general capabilities. Your application — whether it's summarising legal documents, answering customer questions, generating marketing copy, or processing medical records — will have specific patterns that may favour one model over the other in ways that no general benchmark predicts. Test both on your actual workload before committing.
On-Device and Edge Deployment
This is where the comparison becomes most one-sided, and it's worth being direct about it.
Gemma 4 was engineered from the ground up for efficient deployment at the edge. The E2B model runs on Android phones with 6GB of RAM. It runs on a Raspberry Pi. It runs on NVIDIA Jetson embedded hardware. Google built specific optimisations for Android through AICore, and the model integrates with MediaPipe for developers building mobile applications. If you want to know exactly how to get it running on a phone, we wrote a step-by-step guide to running Gemma 4 on Android.
The efficiency numbers support this. Gemma 4 is up to four times faster than Gemma 3 at inference, with up to 60 percent less battery consumption on mobile hardware. These aren't theoretical improvements — they translate directly into a model that feels responsive on a phone rather than painfully slow.
Llama 4, by contrast, is designed for server and cloud deployment. Scout's minimum practical hardware requirement is a machine with at least 32GB of RAM for quantised inference, which puts it in desktop workstation or server territory. Maverick requires multiple GPUs or a high-end accelerator. Meta has not invested in the kind of mobile and edge optimisations that Google has built into Gemma 4.
This isn't a criticism of Llama 4 — it reflects a different design philosophy. Meta's bet is that the most valuable open-source use cases involve running large, capable models on servers, often behind APIs. Google's bet is that meaningful AI capability needs to move to the device itself, for privacy, latency, and cost reasons. Both bets are reasonable. Which one aligns with your needs determines which model matters to you.
For the growing category of applications that need on-device inference — private document processing, offline assistants, embedded AI in products, mobile apps that work without connectivity — Gemma 4 is currently the clear choice. Nothing else in the open-source space matches its combination of capability and efficiency at the edge.
Licencing: The Boring Part That Actually Matters
Licencing is the part of every model comparison that readers skip and lawyers read twice. In this case, the difference is meaningful enough to warrant attention.
Gemma 4 uses the Apache 2.0 licence. This is one of the most permissive open-source licences in existence. You can use the model commercially, modify it, redistribute it, embed it in a product, and there are no conditions beyond preserving the copyright notice. No usage restrictions. No acceptable use policy. No threshold based on company size. Google cannot retroactively change the terms for weights already released.
Llama 4 uses Meta's custom licence, which Meta calls the "Llama Community License." It's permissive for most users but includes two notable conditions. First, companies with more than 700 million monthly active users must request a separate licence from Meta. This affects very few organisations directly, but it introduces a dependency on Meta's discretion at scale. Second, the licence includes an acceptable use policy that prohibits certain applications — weapons development, surveillance, and similar categories. The practical impact of this policy on most developers is minimal, but it means Llama 4 is not, in the strict open-source definition, truly open-source. It's "open-weight with conditions."
For most individual developers, startups, and mid-size companies, both licences are functionally equivalent — free commercial use, no per-token costs. The difference matters for enterprises with large legal teams that scrutinise licence terms, for defence-adjacent or government work where acceptable use policies create compliance questions, and for anyone building a product where the long-term licence stability of the underlying model is a business risk.
Apache 2.0 is a known, tested, legally settled licence. Meta's custom licence is newer and less tested in court. If licence simplicity is a factor in your decision, Gemma 4 has an unambiguous advantage.
Developer Experience and Ecosystem
Llama 4 has the more mature ecosystem, and this matters in practice more than most benchmarks.
Meta has invested heavily in developer tooling, documentation, and community since Llama 2. The result is that in 2026, Llama models have first-class support in virtually every major ML framework and deployment tool: Hugging Face Transformers, vLLM, TensorRT-LLM, Ollama, LangChain, LlamaIndex, and dozens more. When a new deployment tool or optimisation technique launches, Llama support is usually there on day one.
The community around Llama is also larger. More fine-tuned variants exist. More tutorials cover Llama-specific workflows. More production deployments have been written about publicly. When you hit a problem deploying Llama, the chances of someone having documented the solution are higher.
Gemma 4 is newer, and the ecosystem gap is real — though it's narrowing fast. Ollama, Hugging Face, and the major frameworks all support Gemma 4 already. Google's own documentation is solid, particularly for Android and MediaPipe integration. But the volume of community-created resources, fine-tuned variants, and battle-tested deployment guides is still smaller than what Llama offers.
Where Gemma 4's developer experience stands out is in the Google-specific ecosystem. If you're building for Android, using Google Cloud, working with Vertex AI, or deploying through Google's infrastructure, the integration is tighter and better documented than trying to use Llama in the same environment. The AICore Developer Preview for Android, specifically, gives Gemma 4 a development path for mobile apps that Llama simply doesn't have.
The practical advice: if you're deploying on a server and want the widest ecosystem support, Llama 4 has an edge. If you're deploying on Android or Google Cloud, Gemma 4 has the smoother path. If you're using Ollama for local development — which is increasingly the common case for individual developers — both work well and the difference in setup experience is minimal.
Who Should Use Which
Let's be specific rather than diplomatic.
Use Gemma 4 if you need on-device or edge deployment. Nothing else in the open-source space matches it for efficient inference on phones, tablets, embedded hardware, or laptops without dedicated GPUs. The E4B model on a laptop is genuinely capable enough for summarisation, classification, question answering, and code assistance — running locally, with no API costs and no data leaving the machine.
Use Gemma 4 if licence simplicity matters to your organisation. Apache 2.0 is as clean as it gets. No acceptable use policy to evaluate. No company size threshold. No ambiguity.
Use Gemma 4 if you're building for Android specifically. The AICore integration and MediaPipe support create a development path that's substantially easier than trying to run Llama on a phone — which, practically speaking, you can't do with the current Llama 4 models anyway. Our guide to running Gemma 4 on Android covers the full setup.
Use Llama 4 if you need the highest raw capability available in an open model. Maverick, running on appropriate server hardware, is the most capable open-weight model available in 2026 on reasoning, coding, and complex instruction following. If your use case demands the best possible output quality and you have the infrastructure to run it, Maverick is the answer.
Use Llama 4 if you want the largest ecosystem of tools, fine-tuned variants, and community support. The head start matters. When you need a fine-tuned variant for a specific domain — legal, medical, financial — the chances of finding one based on Llama are currently higher.
Use Llama 4 if you need an enormous context window. Scout's 10-million token context window is an order of magnitude larger than Gemma 4's 128,000 tokens. For applications that need to process very long documents — entire codebases, book-length texts, large legal document sets — this is a meaningful architectural advantage.
Use both if you're building a system with multiple tiers. A growing pattern in production deployments is using a smaller, efficient model for routine tasks and routing complex queries to a larger model. Gemma 4 E4B handling simple queries locally, with Llama 4 Maverick available via API for complex ones, is a defensible and increasingly common architecture.
The Bigger Picture
The fact that this comparison is difficult is itself the story. Two years ago, open-source AI models were interesting experiments that couldn't match proprietary offerings on any serious task. In 2026, Gemma 4 and Llama 4 are both production-grade tools that handle a wide range of real-world applications competently. The gap between open and proprietary hasn't closed, but it's narrowed to the point where the decision to self-host is now driven by practical considerations — privacy, cost, latency, control — rather than being blocked by inadequate capability.
Google and Meta are releasing these models for strategic reasons, not altruistic ones. Google wants developers building on Gemma to stay in the Google ecosystem. Meta wants Llama to become the default infrastructure layer for AI, strengthening Meta's position even if competitors build on their models. Understanding these motivations helps you assess the long-term risk: both companies have incentives to continue investing in their open releases, but those incentives could shift.
For developers and companies making a choice today, the practical framework is simple. Where does the model need to run? How complex are the tasks? How much does licence certainty matter? Answer those three questions and the right choice becomes clear.
For a deeper look at Gemma 4 specifically — including setup instructions, hardware requirements, and benchmark details — see our complete Gemma 4 guide.