Gemma 4 vs Llama 4.1: Benchmarks, Speed, License (2026)

Meta refreshed its flagship open model with Llama 4.1 in April 2026 — an incremental upgrade over Llama 4 Maverick with better coding and instruction following. Meanwhile Google's Gemma 4 has settled in as the go-to choice for anyone who actually has to run a model on their own hardware. If you're picking between them today, here's the honest breakdown.

Quick Comparison

Short version: Gemma 4 wins anything touching mobile, multilingual, or open licensing. Llama 4.1 wins raw benchmark peaks and long-context work — if you have the GPUs to run it.

Benchmark Deep Dive

Numbers from published April 2026 results, FP16 precision unless noted:

Llama 4.1 takes every category on raw score. But note the size gap: Gemma 4 31B reaches ~92–95% of Llama 4.1 400B's quality at roughly 1/12 the parameter count. Per-dollar-of-compute, Gemma 4 usually wins.

For language-specific work, Gemma 4 is in a different league:

• Chinese (C-Eval): Gemma 4 achieves ~84%, Llama 4.1 ~72%
• Japanese (JGLUE): Gemma 4 ~81%, Llama 4.1 ~68%
• Indonesian / Vietnamese / Thai: Gemma 4 consistently within ~5pt of English; Llama 4.1 drops 15–25pt

Hardware Requirements

Running Gemma 4

Running Llama 4.1

The 400B MoE variant doesn't fit on consumer hardware at any quantization. If you're running locally, you're effectively comparing Gemma 4 31B vs Llama 4.1 70B, and the comparison becomes much closer.

Inference Speed

Same hardware, both at 4-bit quantization:

Gemma 4 is ~2× faster at its comfortable size, and fits where Llama 4.1 70B won't.

When to Pick Which

Pick Gemma 4 if:

• You're deploying on phones, laptops, or anywhere without a datacenter GPU
• Your users speak anything other than English
• You need multimodal (audio, video) — Llama 4.1 can't do it
• You want Apache 2.0 freedom (no license review, no user-count cap)
• You care about per-dollar quality

Pick Llama 4.1 if:

• You need the absolute top MMLU / HumanEval numbers
• You're processing documents longer than 256K tokens (10M context is genuinely useful for huge codebases)
• You have multi-GPU infrastructure already
• English-only workload where the multilingual edge doesn't matter

Deployment

Gemma 4 via Ollama

ollama pull gemma4:31b
ollama run gemma4:31b

Or for on-device work, see our mobile deployment guide for E2B/E4B on iPhone and Android.

Llama 4.1 via Ollama

ollama pull llama4.1:70b
ollama run llama4.1:70b

The 400B MoE variant ships through cloud providers (Meta, AWS Bedrock, Azure) rather than local Ollama at this writing.

Cost Comparison

Self-Hosting (first year)

Gemma 4 31B:

• Hardware: RTX 4090 ~$1,800
• Power: ~$35/month
• Year 1 total: ~$2,220

Llama 4.1 70B:

• Hardware: 2× RTX 4090 or single A100 ~$4,200 / $15,000
• Power: ~$90/month
• Year 1 total: ~$5,280 (2× 4090 path)

API Pricing (per million tokens, April 2026)

At equivalent output quality, Gemma 4 self-hosted undercuts both Llama 4.1 tiers within 3–6 months for any sustained workload.

Migration Notes

From Llama 3.x / Llama 4 → Llama 4.1: Mostly drop-in. Tokenizer is backward-compatible. Expect 10–15% quality bump on coding and reasoning.

From Gemma 2 / Gemma 3 → Gemma 4: Tokenizer updated. Native function calling replaces ad-hoc JSON parsing. For details see Gemma 4 architecture changes.

Cross-family migration (Gemma → Llama or vice versa): Fine-tunes don't port directly. Budget 1–2 weeks of re-tuning if you have a production fine-tune to migrate.

FAQ

Which is better for coding?

Llama 4.1 scores higher on HumanEval (85.4% at 70B, 89.1% at 400B vs Gemma 4's 82.7%). If you're writing complex algorithms or doing deep code refactoring, Llama 4.1 400B is measurably better — when you can run it. For everyday coding on a laptop, Gemma 4 31B is close enough and actually runnable.

Can I run these on a MacBook?

Gemma 4 E2B and E4B run smoothly on any Apple Silicon Mac. Gemma 4 26B MoE / 31B Dense need an M2 Max or M3 Pro with 32GB+. Llama 4.1 70B needs an M3 Ultra with 64GB+ and runs at ~8 tok/s. Llama 4.1 400B is not practical on any Mac.

Which handles Chinese, Japanese, or Korean better?

Gemma 4, by a wide margin. Native 140-language training vs Llama 4.1's 28. Real-world benchmarks (C-Eval, JGLUE, KLUE) show 10–15 point gaps in Gemma 4's favor.

What about commercial use?

Gemma 4 is Apache 2.0 — no restrictions, no user-count cap, no revenue threshold. Llama 4.1 uses Meta's Llama License, which requires a separate commercial license if your product has 700M+ monthly active users (not an issue for 99.9% of teams).

Which hallucinates less?

Llama 4.1 400B scores slightly higher on TruthfulQA (72.3% vs Gemma 4 31B's 68.9%), but this gap disappears at equivalent parameter counts. For most use cases the difference is within margin of noise.

Will there be a Gemma 5?

Google hasn't announced a Gemma 5 timeline as of April 2026. Expect continued Gemma 4 point releases (multimodal improvements, longer context) before a major version bump.

Related Reading

• Gemma 4 vs Llama 4 (Maverick) — the original comparison if you're on Llama 4 and wondering whether to upgrade
• Gemma 4 Benchmarks Deep Dive — all the benchmark numbers in one place
• Gemma 4 26B vs 31B — MoE vs Dense within the Gemma 4 family
• Gemma 4 Mobile Deployment — running E2B/E4B on phones
• How to Run Gemma 4 with Ollama — start here if you're new

Bottom Line

For 90% of developers picking an open LLM in April 2026, Gemma 4 is the default answer. It runs on hardware you already own, speaks your users' languages, and ships under a license your legal team won't ask questions about.

Llama 4.1 is the right pick when you specifically need: (1) the highest possible English benchmark scores, (2) 10M-token context, or (3) already-built multi-GPU infrastructure where the 400B MoE variant makes sense. Outside those cases, it's overkill.

Last updated: April 18, 2026. Benchmarks from official releases and community testing.