Google DeepMind launched Gemma 4 on April 2, 2026. Meta released Llama 4 Scout and Maverick on April 5, 2025 — nearly a year earlier. If you are a tech startup trying to decide which open model to use, the timing makes this less of a simultaneous launch and more of a generational comparison.

Here is the truth about these models.

What is an “Open” Model in 2026?

Before we compare these models, let us talk about what it means as the term “open” has been used a lot this year.

Q: Are Gemma 4 and Llama 4 open source?

No, not in the traditional sense. Both models make their weights publicly available. They do not release their full training code and data like Linux or PostgreSQL does. An accurate term would be open-weight.

The difference in licensing is important for startups:

Gemma 4 uses the Apache 2.0 license. There are no usage restrictions, no limits on active users, and no need to give credit. It is fully commercial.

Llama 4 uses Meta’s custom Llama 4 Community License. Commercial use is allowed for startups, but there is a limit. Apps with over 700 million active users need a separate agreement with Meta. Some restrictions make it hard for companies in the EU to use it without workarounds.

For startups, both licenses are okay. If you are building something big or working in the EU, Gemma 4 is a cleaner choice.

The Models at a Glance

Gemma 4 is from Google DeepMind.

It was released on April 2, 2026. There are four model sizes: E2B, E4B, 26B MoE, and 31B Dense. The 26B MoE model is special. It only uses 3.8B parameters per pass, which means it can run on a 16GB GPU with quantisation. It is still good at reasoning. Can compete with bigger models.

What makes Gemma 4 special:

1. It can handle types of data like text, images, video, and audio on smaller models.
2. It uses the Apache 2.0 license, so there are no legal surprises.
3. The E2B model can run on a smartphone. The 26B MoE model can run on a consumer-grade workstation.
4. There have been over 400 million downloads of all Gemma models on Google’s developer portal.

Llama 4 is from Meta.

It was released on April 5, 2026. There are two variants: Scout and Maverick. A third model, Behemoth, has not been released yet.

What makes Llama 4 special:

1. Scouts’ 10 million token context window is the best among open-weight models. That is like 15,000 pages of text in one prompt.
2. Maverick can compete with GPT-4o on benchmarks.
3. It was trained on over 200 languages and has good multilingual coverage.
4. It works with Meta AI on WhatsApp, Messenger, and Instagram.

Head-to-Head: The Benchmarks That Actually Matter

Let us be honest about what benchmark scores mean for a startup: they are like signs, not guarantees. A small difference in scores does not usually change the real-world quality of a product. A big difference usually does.

Here are some benchmarks:

Across every major benchmark, Gemma 4 31B outperforms Llama 4 Scout. On AIME 2026, which tests hard math reasoning, Gemma 4 scores 89.2% against Scout’s 88.3%. The gap widens on GPQA Diamond, a graduate-level reasoning test, where Gemma 4 leads 84.3% to 57.2%. For real-world coding tasks on LiveCodeBench v6, Gemma 4 scores 80.0% compared to Scout’s 77.1%. On MMLU Pro, which covers general knowledge, Gemma 4 scores 85.2% while Scout scores 74.3%. Finally, on Arena AI ELO, which measures human preference, Gemma 4 scores 1452 (31B) and 1441 (26B MoE), while Maverick — not Scout — scores 1417. It’s worth noting that Maverick scores are not Scout’s, and Scout trails on reasoning benchmarks across the board.

Maverick scores are not Scout. Scout trails on reasoning benchmarks.

The key thing to know: Gemma 4 31B is better than Llama 4 Scout on every reasoning and coding benchmark. The GPQA Diamond gap. 84.3% Vs 74.3% is a difference in graduate-level reasoning. For startups building AI assistants, coding tools, or data analysis features, that gap will show up in production.

Where Llama 4 wins is context. Scouts’ 10M token window is the best.

The Question Startups Actually Ask

Q: Which model should we use for an AI coding assistant?

Gemma 4. It scores 80% on LiveCodeBench v6, which is better than Llama 4 Scouts 77.1%. The 26B MoE variant gives you the same quality at 3.8B active parameters, which means lower costs per call. If your team uses Apple Silicon, Gemma 4s 26B MoE is a choice for local development.

Q: We are building a compliance tool that needs to process big contracts and case histories. Which one?

Llama 4 Scout. There is no choice when you need a 10M token context. A year’s worth of contracts, regulatory filings, or email threads, all in one session without chunking. No other open-weight model comes close. You need datacenter hardware. At least a single H100.

Q: We need to run the model on a device for privacy reasons.

Gemma 4. The E4B model runs on an 8GB laptop. The 26B MoE runs on 18GB RAM. Llama 4 Scout needs at least 70GB VRAM. There is no way to run it on a consumer GPU. Gemma 4 is the choice for on-device use at this level.

Q: We are an EU-based startup. Which model can we use without risk?

Gemma 4. Metas Llama 4 Community License has restrictions that affect EU companies. Gemma 4s Apache 2.0 license does not have these restrictions.

Q: Which model is easier to tune for specific tasks?

Both models support LoRA tuning. Gemma 4 has day-0 support across tools like Llama.cpp, Ollama, MLX, Hugging Face Transformers, and SGLang. The community says Gemma 4 is easier to work with out of the box.

The Architecture Behind the Numbers

Both models use a Mixture-of-Experts architecture. This lets models store knowledge that they use on each query. It is like a hospital with specialists, only the relevant ones work on each patient.

The efficiency story is most dramatic with Gemma 4s 26B MoE: 25.2 billion parameters, only 3.8 billion active per token. It achieves 97% of the 31B models’ quality at roughly 8x less compute per inference step. For startups running high-volume inference, that is a deal.

Llama 4 Scouts’ MoE design is also 17B active out of 109B total because the base model is bigger, and the hardware requirements are higher.

Cost Reality Check

Hosting costs are where “open” models get complicated. Weights are free. Compute is not.

1. Gemma 4 26B MoE:

• Local (M2 MacBook Pro 36GB): Okay for development and testing.
• Cloud (via OpenRouter or Together AI): $0.20–0.40 per million tokens.
• Self-hosted on a single A100 80GB: Production-ready.

2. Llama 4 Scout:

• Cloud API (via Groq, Together AI): Approximately $0.10–0.20 per million tokens.
• Self-hosted: needs at least 55GB VRAM. A single H100 80GB costs ~$2,000–3,000/month on cloud GPU providers.

3. Llama 4 Maverick:

• Self-hosted: Needs 8× H100. ~$17,000–23,000/Month. This is an infrastructure investment.
• Via API: ~$0.19–0.49 per million tokens on distributed inference.

For early-stage startups, using the hosted API route makes sense for both models. The self-hosting conversation changes once you hit roughly 500M–1B tokens per month.

What About Multimodal?

Both models process text and images natively. Beyond that, the capabilities are different:

Gemma 4: text + images + video + audio (all sizes). Audio (E2B and E4B edge models only). No adapters needed.

Llama 4: text + images + video. No native audio support.

If your product roadmap includes voice input, Gemma 4s edge models with audio are the only open-weight option at that size tier.

The Verdict: Which Model Actually Wins?

There is no winner. There is a right answer for each use case.

1. Choose Gemma 4 if:

• You need the reasoning, math, and coding performance per active parameter.
• You are building for on-device, edge, or privacy-sensitive deployments.
• You are EU-based or need Apache 2.0 licensing clarity.
• You want native audio support in smaller models.
• You are starting an agentic workflow.

2. Choose Llama 4 Scout if:

• Your use case needs a processing context.
• You are already invested in Meta’s ecosystem and tooling.
• You need 200+ language support with strong multilingual coverage.

3. Choose Llama 4 Maverick if:

• You need GPT-4o class performance and want to self-host at scale.
• You have the infrastructure budget for H100 setups, or are comfortable with API pricing.

For most startups building AI products in 2026, coding assistants, document tools, customer-facing chatbots, and data extraction pipelines, Gemma 4 is the default starting point. The Apache 2.0 license removes every barrier. The 31B Dense and 26B MoE variants deliver frontier-level performance at infrastructure costs. The edge model story opens product categories that were not viable with previous-generation open models.

The only scenario where Llama 4 wins outright is the long-context niche. It wins that niche decisively, and nothing else comes close to 10M tokens.

TL;DR

Gemma 4 wins on reasoning, coding, on-device deployment, and licensing (Apache 2.0 with no restrictions). Llama 4 Scout wins only one thing, but wins it decisively: a 10 million token context window, ideal for processing massive documents. For most startups, Gemma 4 is the default. Pick Llama 4 Scout only if your use case genuinely needs that long context.

Looking to build a high-performing remote tech team?

Check out MyNextDeveloper, a platform where you can find the top 3% of software engineers who are deeply passionate about innovation. Our on-demand, dedicated, and thorough software talent solutions provide a comprehensive solution for all your software requirements.

Visit our website to explore how we can assist you in assembling your perfect team.