Llama 4 vs Gemini: Run Llama Locally or Use the Gemini API

Published October 15, 2025 • Rewritten November 2, 2026 • 9 min read

Short answer: Gemini is the stronger general model and needs no hardware; Llama 4 is the one you can run yourself. Pick Llama 4 when data must stay on your machines or you have steady, high volume and the hardware to match. Pick Gemini (now Gemini 3, released November 18, 2025) when you want top quality without owning a GPU. If you want a Google model on your own hardware, the answer is Gemma, not Gemini.

Correction notice. An earlier version of this page said Llama 4 is Apache 2.0 licensed and quoted MMLU scores, a total-cost-of-ownership model and API prices that had no source. Llama 4 uses the Llama 4 Community License, and the unsourced figures are gone. Every number below comes from Meta, Google, LMArena or the Ollama registry.

The Models Side by Side

Sources: Meta's "The Llama 4 herd" announcement (April 5, 2025); Google's Gemini announcements; Ollama library sizes checked November 2, 2026. Meta also previewed a much larger Llama 4 Behemoth as a teacher model; it was not released as downloadable weights.

Licensing: The Part the Old Version Got Wrong

Llama 4 is not Apache 2.0. It ships under the Llama 4 Community License, which in practice means:

• Free to download, run, fine-tune and build products on, including commercial products
• The 700M clause: if your products had more than 700 million monthly active users on the release date, you must request a separate license from Meta
• Attribution: products built on Llama 4 must display "Built with Llama", and fine-tuned models must keep "Llama" at the start of their name
• Acceptable Use Policy: Meta's AUP applies to everything you build

For most companies and individuals, that is effectively free use. It is still not an OSI-approved open-source license, and your legal team should read it rather than assume Apache terms. Gemini has no weights to license at all: you use it under Google's API and product terms, and your prompts are processed on Google's servers.

For a side-by-side of how other open models are licensed, see our LLM licences for commercial use guide.

Performance: What Is Actually Known

Neither Meta nor Google publishes a head-to-head of these exact models, so this page does not invent one. What is on the record:

• Gemini 2.5 Pro launched at #1 on LMArena (LMArena, March 25, 2025).
• Meta's launch post cited an LMArena ELO of 1417 for Maverick, placing it just under Gemini 2.5 Pro. The model on the leaderboard was Llama-4-Maverick-03-26-Experimental, an "experimental chat version" tuned for conversation, not the weights Meta released.
• LMArena objected. It said Meta's interpretation of its policy "did not match what we expect from model providers", published the battle data, and tightened its rules. The released Maverick then ranked well below the experimental version.
• Gemini moved on. Google released Gemini 3 Pro on November 18, 2025, so Gemini 2.5 is no longer Google's flagship.

If you searched for llama-4-maverick-03-26-experimental vs gemini-2.5-pro-exp-03-25, that is the whole story: the experimental Maverick came close to Gemini 2.5 Pro on human-preference votes, but you cannot download that model. For current standings, check our AI model leaderboard and our explainer on how LMArena ranks models.

Cost: Hardware You Own vs Tokens You Rent

The two cost models are different in kind, so compare them with your own numbers rather than a generic total-cost table:

• Llama 4 locally: the cost is hardware plus electricity, and it does not grow with usage. The catch is size: Scout is a 67GB download and Maverick 245GB, so the entry ticket is a large unified-memory machine or a multi-GPU box (see the next section).
• Gemini via API: you pay per input and output token, with prices that vary by model and prompt length, and Google offers a free tier in AI Studio for experimentation. Check Google's current Gemini API pricing page before you budget; prices have changed several times since 2025.
• Rule of thumb: low or bursty volume favours the API; steady high volume on one workload, or data that cannot leave your network, favours owning the hardware.

Our AI cost calculator does this sum with your real token volume.

Running Llama 4 Locally

# Llama 4 Scout: 67GB download, needs roughly 80GB+ of combined VRAM and RAM
ollama pull llama4:scout
ollama run llama4:scout

# Llama 4 Maverick: 245GB download, workstation or server territory
ollama pull llama4:maverick

Both are mixture-of-experts models: only 17B parameters are active for each token, so they generate faster than their total size suggests, but memory must still hold all of the weights. Realistic homes for Scout are 96GB and 128GB unified-memory machines (Apple Silicon or AMD Strix Halo mini PCs) or a GPU with partial CPU offload. Our Llama 4 local setup guide walks through hardware, quantization and multimodal prompts, and the Llama 4 Scout model page has the specs.

Want a Google Model Locally? Use Gemma

Gemini never leaves Google's data centres, but Google's open-weights Gemma 4 family is in the Ollama library:

A ceiling is memory bandwidth divided by file size, the most the card can physically generate; real speed lands below it. Details in our Gemma 4 guide.

Meta's own single-GPU option is now Muse Glimmer (muse-glimmer:30b, 18GB, Apache 2.0 per its Ollama listing), which fits a 24GB card where Llama 4 Scout does not. See the Muse Glimmer 30B page and our best Ollama models list for where it ranks.

Which One for Which Job

Frequently Asked Questions

Is Llama 4 better than Gemini? Not as a general assistant; Gemini 2.5 Pro led LMArena at launch and Gemini 3 has replaced it. Llama 4 wins on control: downloadable weights, no per-token bill, data stays local.

Is Llama 4 Apache 2.0? No. It is the Llama 4 Community License: free for almost everyone, with a 700M-monthly-active-user clause, "Built with Llama" attribution and Meta's Acceptable Use Policy.

Can I run Gemini locally? No. Use Gemma 4 (gemma4:26b or gemma4:31b) if you want a Google model on your own machine.

What hardware does Llama 4 Scout need? About 80GB or more of combined memory for the 67GB download, such as a 96GB+ unified-memory machine.

What happened with Llama 4 on LMArena? Meta's 1417 ELO came from an experimental chat-tuned version, not the released weights. LMArena objected and changed its rules; the released model ranked lower.

Strategic Decision Framework

1. Can the data leave your network? If not, the decision is made: run Llama 4 Scout, Gemma 4 or another open model locally.
2. Do you already own, or will you buy, 80GB+ of memory? If not, Llama 4 is out of reach locally; Gemma 4 or Muse Glimmer on a 24GB GPU is the practical local option.
3. Is your volume steady and high? Then price the hardware against a year of API tokens in the cost calculator.
4. Otherwise, start with the Gemini API free tier, measure your usage, and revisit local once the bill or the privacy question makes it worth it.

For a broader comparison of running models locally versus paying for ChatGPT-style services, see local AI vs ChatGPT.

Official sources: Meta Llama · Llama 4 license · Google AI for Developers · Ollama: llama4