Google Gemma 4 vs Meta Llama 3

Quick verdict

Google Gemma 4 (April 2026) is Google DeepMind’s open-weight family built from the same research line as Gemini 3: edge E2B/E4B models, a later 12B dense multimodal checkpoint, a 26B Mixture-of-Experts optimized for latency (~3.8B parameters active per token), and a 31B dense quality model. Context spans roughly 128K on edge SKUs and up to about 256K on the larger models. Inputs are multimodal (text and images across the family; audio on E2B/E4B). The decisive product move is the license: Apache 2.0, after earlier Gemma generations used a custom terms-of-use style license.

Meta Llama 3 (April 2024) and its 3.1 / 3.2 / 3.3 line remain the industrial open-weight default. Original Llama 3 shipped 8B and 70B; Llama 3.1 added 128K context and a 405B flagship; 3.2 brought small edge models plus vision; 3.3 refreshed the 70B text instruct line. The ecosystem — GGUF quants, LoRA recipes, cloud dropdowns, Guard tooling — is unmatched. The tradeoff is legal shape: Meta’s Community License allows broad commercial use for most teams but is not OSI Open Source (700M MAU gate, acceptable-use policy, naming/attribution rules).

Pick Gemma 4 when you want 2026 intelligence-per-parameter, true Apache clarity, native multimodal/edge, and local agents without Meta’s license shape. Pick Llama 3.x when you need the largest open-ish weights, the widest host and fine-tune surface, or you already standardized on the Llama stack and procurement path.

Side-by-side

What Gemma 4 actually is (2026)

Gemma 4 is Google’s open companion to Gemini: downloadable weights you can run offline, fine-tune, and redistribute under Apache 2.0. Google positions the family for advanced reasoning and agentic workflows — function calling, structured JSON, system instructions — not only chat. Cumulative Gemma downloads exceeded 400 million with 100k+ community variants before Gemma 4; the “Gemmaverse” framing is deliberate ecosystem play (BgGPT-style language specializations, research adaptations, and now Apache-cleared commercial products).

Size map that matters in practice:

• E2B / E4B — effective ~2B / ~4B active footprint for phones, Raspberry Pi, Jetson, AI Edge Gallery, and Android AICore paths; multimodal with audio on edge SKUs.
• 12B dense — mid-2026 unified encoder-free multimodal mid-size for developer machines; QAT variants target comfortable 16GB-class setups when quantized.
• 26B MoE — latency-oriented; only ~3.8B parameters active per token; popular “daily driver” on consumer GPUs when quantized (Unsloth/GGUF packs are common).
• 31B dense — quality-first single-GPU class model. Unquantized bfloat16 is described as fitting an 80GB H100; quantized builds run on high-end consumer cards. At launch, Google cited Arena open-model ranks near the top of the open chat board for the 31B (#3 open) and 26B (around #6).

Day-one stack coverage is unusually complete: Hugging Face Transformers, llama.cpp, MLX, vLLM, SGLang, Ollama, LM Studio, Unsloth, NVIDIA NIM/NeMo, Docker, Keras, LiteRT-LM, Vertex AI / GKE / Cloud Run, and Android Studio agent mode.

What Llama 3 actually is

Llama 3 launched as Meta’s 8B and 70B pretrained + instruct models, trained on over 15T tokens from public sources, with a 128K tokenizer and grouped-query attention for efficiency. Meta’s “release early and often” cadence produced a family rather than a single checkpoint:

• Llama 3 (Apr 2024) — 8B and 70B text; 8K context at launch.
• Llama 3.1 (Jul 2024) — 8B/70B upgrades + 405B; 128K context; multilingual emphasis; license change allowing outputs to improve other models.
• Llama 3.2 (Sep 2024) — 1B/3B edge text models; 11B/90B vision-language models.
• Llama 3.3 (Dec 2024) — 70B text instruct refresh aimed at quality without jumping to 405B hardware.

The product is a platform default: accept the license on Hugging Face, pull weights, and you inherit years of GGUF quants, LoRA recipes, eval harnesses, and vendor endpoints (AWS Bedrock, Azure, GCP Vertex, Databricks, Snowflake, NVIDIA NIM, Groq, Together, Fireworks, Replicate, and a long tail of inference startups).

Meta also ships a responsibility stack — Llama Guard, Prompt Guard, CyberSec Eval, Code Shield, Responsible Use Guide — that enterprises often treat as part of the “Llama system,” not just the base model. That package is a real differentiator when security and compliance reviews care about reference architectures, not only raw chat Elo.

Pricing and real cost (TCO)

Neither family is a SaaS subscription with a monthly seat price. Both are primarily weights + your compute (or someone else’s GPU bill).

Gemma 4 money map:

• Weights: free under Apache 2.0.
• Local: electricity + GPU/NPU. E2B/E4B target phones and small devices. Quantized 12B/26B MoE often fits 16–24GB cards; 31B wants more VRAM or aggressive quant. QAT official posts list expected VRAM for compressed builds (for example Q4-class 12B around single-digit GB).
• Hosted Google: AI Studio for try-out; Vertex Model Garden / GKE / Cloud Run for production — pay GCP GPU/TPU or managed serving rates, not a special “Gemma license fee.”
• Third-party hosts: NIM, Baseten, and other OpenAI-compatible providers that list Gemma 4. Coverage is growing but is still thinner than Llama’s commodity API market.

Llama 3 money map:

• Weights: free for most commercial use under the Community License, subject to AUP and the 700M MAU clause.
• Local: 8B is the commodity laptop/desktop model; 70B is multi-GPU or heavy quant; 405B is cluster-class or hosted-only for almost everyone.
• Hosted APIs: deep price competition. Public 2025–2026 host lists commonly put Llama 3.1 8B Instant in the ~$0.05–$0.20 per million token band depending on provider (Groq often on the low end for latency-optimized SKUs; Together/Fireworks in a similar serverless band for larger models; Llama 3.3 70B often around ~$0.6–$1.0 per million tokens blended depending on host). Rates change frequently — verify the host’s pricing page before budgeting.

Hidden costs that dominate TCO: quant quality (broken GGUFs cost days), agent harness template bugs, eval regression when you swap families, compliance review of Meta’s custom license vs Apache, and memory for long context (Gemma 4 users repeatedly call KV cache a “memory hog”). Legal review is essentially free for Apache; budget counsel time for Llama if you are large, regulated, multi-affiliate, or near Meta-scale MAU.

What people actually say (Reddit, HN, field notes)

Gemma 4 praise: LocalLLaMA threads call 26B/31B strong for conversation, creative writing, multilingual work (including non-English reports such as Finnish), and agent coding that “just finishes” where some peers loop. Arena preference Elo is treated as more meaningful than some synthetic bench wins for competing open families. The HN launch thread was huge; edge-on-iPhone, LM Studio headless + agent CLI, and Codex-local experiments got real engagement. Unsloth/QAT GGUF writeups claim very high tok/s on a single 4090 after backend fixes. Some users describe Gemma 4 as the most “Claude-like” local model they have run on Apple Silicon.

Gemma 4 complaints: Context is expensive in VRAM; early llama.cpp / LM Studio support was rocky with tokenizer bugs; some users see heavy safety refusals on medical or edge topics; multi-turn tool-calling can still fall apart after a few vibecoding turns versus tuned Qwen-class agents; quality versus the latest Chinese open MoEs remains contested for pure coding agents. Speed claims for E2B/E4B versus Llama 3.3 are disputed in r/LLMDevs.

Llama 3 praise: Years of “just works” infra. License accept on HF, Ollama tags, and cloud dropdowns everywhere. 3.1 405B remains the large open-ish ceiling many orgs still reference for distillation and synthetic data. Community knows the failure modes and has Guard/finetune recipes. Host benchmarks treat Llama SKUs as the default latency/price yardstick.

Llama 3 complaints: OSI, FSF, and legal blogs still hammer that “open source” marketing is wrong — MAU threshold, field-of-use friction, AUP, and (on some multimodal lines) EU domicile restrictions on license grants. By mid-2026, pure quality-per-parameter for 8B/70B trails newer families (Gemma 4, Qwen, etc.) for many vibes-based local users. The 405B is excellent but operationally heavy.

When Gemma 4 wins

• You need Apache 2.0 for counsel, redistribution, embedded products, or sovereign “we own the weights” narratives.
• You care about intelligence per GB of VRAM in the 12–35B class in 2026.
• You want native multimodal + long context without bolting a separate vision stack.
• Edge / Android / offline phone or Raspberry Pi is a real product target (LiteRT, AICore, AI Edge Gallery).
• You’re already on Vertex, GKE, or Google AI Studio and want one open model path alongside Gemini.
• Agent tools + JSON + system prompts matter more than 2024-era fine-tune archives.

When Llama 3 wins

• You need the 405B-class open-weight ceiling or a mature 70B production recipe already signed off by security.
• Your stack, evals, LoRAs, vendor contracts, and runbooks already say “Llama.”
• You want the cheapest multi-host API market and maximum commodity ops knowledge.
• You’re under 700M MAU and counsel already signed off on Meta’s Community License.
• You depend on Llama Guard / Purple Llama-style safety components as a package.
• You need maximum historical fine-tune and quant availability for a specific 8B/70B niche (medical, legal, local language adapters built since 2024).

Risks and failure modes

• Gemma 4: Newer kernels/templates still churn; long-context RAM spikes; safety refusals; tool-call format drift across backends; don’t assume free AI Studio quotas equal production capacity.
• Llama 3: License is not OSI open source; mega-platforms need Meta’s discretion; naming/attribution obligations on derivatives; older small models may underperform 2026 peers; multimodal license grants can have regional limits on some lines — read the exact license for your SKU.
• Both: Benchmarks ≠ your agent harness. Quant choice (Bartowski/Unsloth/etc.) can swing quality more than base family branding. Hallucinations, prompt injection, and insecure code suggestions remain user problems either way — plan filters and evals.

Recommendation by profile

FAQ

Is Gemma 4 free?
Yes for weights under Apache 2.0. Compute (local GPU or cloud) is not free. Hosted Google surfaces bill under Google’s service terms.

Is Llama 3 open source?
It is open weights with a custom Community License. OSI and FSF analyses conclude it is not Open Source / free software because of MAU gates, use restrictions, and related constraints.

Which is better for coding agents?
Field reports in 2026 often favor Gemma 4 26B/31B for local agent quality-per-VRAM, but harness quality and quant matter more than brand. Llama 70B/405B still has deeper historical tool recipes and host coverage. Some users still prefer heavily tuned Qwen-class MoEs for multi-turn tool use — run your own eval suite.

Can I use either commercially?
Gemma 4: generally yes under Apache 2.0. Llama 3: generally yes if you comply with the Community License and AUP and are under the MAU threshold (and any regional clauses for your exact model card).

What about Llama 4?
This page compares the Llama 3 family (3 / 3.1 / 3.2 / 3.3) to Gemma 4 — the matchup people search when choosing a current open-weight baseline. If a newer Llama generation is your target, re-check Meta’s latest license and model cards separately.

How big a machine do I need?
E2B/E4B: phone/RPi/laptop. 8B Llama or quantized Gemma 12B/26B: single consumer GPU class. 31B dense / 70B Llama: high VRAM or multi-GPU/quant. 405B: serious cluster or hosted only.

Where do I download weights?
Gemma: HF collection, Kaggle, Ollama. Llama: HF after license accept, llama.com, or Ollama tags (llama3, llama3.1, llama3.2, llama3.3).

Does Gemma 4 replace Gemini?
No. Google positions Gemma as the open, on-device/self-host line and Gemini as the proprietary frontier API. Many teams use both.

Is the 700M MAU clause a real risk for startups?
Usually not on day one. It becomes material for large multi-product platforms, affiliates that share MAU counts, and companies near hyperscaler scale. Counsel should read the exact license version tied to your checkpoint.

Sources & research basis

This comparison is grounded in 150 unique sources: Google’s Gemma 4 launch posts, DeepMind product pages, model cards, Apache license text, Vertex/GKE/Cloud Run/Android docs; Meta’s Llama 3 / 3.1 / 3.2 / 3.3 blogs, Community License and AUP texts, Purple Llama/Guard materials, and research paper; Hugging Face model cards and blogs; Ollama/Unsloth/vLLM/LM Studio/NVIDIA/Docker docs; Ars Technica, TechCrunch, The Verge, VentureBeat coverage; OSI/FSF/legal analyses of Apache vs Llama Community License; multi-thread Reddit LocalLLaMA/ollama/LLMDevs sentiment; Hacker News launch, local-run, and license threads; independent reviews, host pricing pages (Together, Groq, Fireworks, Artificial Analysis), and video deep dives. Full URL pack: research_cache/google-gemma-4-vs-meta-llama-3_sources.json (150 entries).

Bottom line

In mid-2026, Gemma 4 is the better default for new local and productized open-weight work if you care about license clarity, multimodal edge, and quality at roughly 4–31B scale. Llama 3.x remains the better default if your organization already runs on Meta’s ecosystem, needs 405B-class weights, optimizes for commodity host pricing, or wants the mature Guard/recipe surface. Many teams will run both: Llama where procurement and hosts are locked; Gemma where Apache and on-device intelligence win the design review. Decide with your eval suite and counsel — not vendor leaderboards alone.