Is Gemma 4 better than Llama 4?

On quality per parameter, yes. Gemma 4 31B scores 89.2% on AIME 2026 and 80.0% on LiveCodeBench v6, beating Llama 4 (88.3% / 77.1%) despite being an order of magnitude smaller. Llama 4 still wins when you need the largest open model with strong 10M-token context, but Gemma 4 is the better choice for local and edge deployment.

Which open model is best for local / on-device use?

Gemma 4 E2B and E4B. They run on a single consumer GPU or even an Apple Silicon Mac, ship Apache 2.0, and include native multimodal (text + vision + audio). Qwen 3.5 is a close second, especially for coding, and has slightly better long-context recall on its smaller sizes.

Which open model is best for coding?

Qwen 3.5 Coder remains the best coding-specific open model, but Gemma 4 31B has closed the general-coding gap (80.0% LiveCodeBench v6). For pure coding workloads, pick Qwen 3.5 Coder; for general-purpose + coding + multimodal, pick Gemma 4 31B.

What licenses do these models use?

Gemma 4 ships under Apache 2.0 (fully open, including commercial use with no MAU cap). Qwen 3.5 is Apache 2.0 for the base models. Llama 4 still uses the Llama Community License, which has a 700M monthly active user cap and other restrictions — not truly open source by OSI definition.

Quick Answer

Gemma 4 vs Qwen 3.5 vs Llama 4: Open Models April 2026

Published: April 19, 2026

Gemma 4 vs Qwen 3.5 vs Llama 4: Open Models April 2026

Google shipped Gemma 4 on April 2, 2026, under Apache 2.0 — and it just redefined what “small open model” means. With Qwen 3.5 still holding coding supremacy and Llama 4 anchoring the large end of the open-weight space, here is how the three actually compare for self-hosting in April 2026.

Last verified: April 19, 2026

TL;DR

Factor	Winner
Quality per parameter	Gemma 4
Coding	Qwen 3.5 Coder
License freedom	Gemma 4 / Qwen 3.5 (Apache 2.0)
Largest open model	Llama 4
Multimodal out of the box	Gemma 4
Long-context recall (small sizes)	Qwen 3.5
Local on 16GB Mac	Gemma 4 E4B
Arena ranking	Gemma 4 31B (#3 open)

Benchmarks (April 2026)

Benchmark	Gemma 4 31B	Qwen 3.5 35B	Llama 4 400B
AIME 2026 (math, no tools)	89.2%	86.7%	88.3%
LiveCodeBench v6	80.0%	82.4%	77.1%
MMLU-Pro	82.1%	80.8%	81.5%
GPQA Diamond	75.3%	73.6%	74.8%
MMMU (vision)	76.9%	72.1%	70.4%
Long-context recall (128K)	92%	95%	94%
Arena Elo (open)	#3	#4	#5

Gemma 4 31B leads on math, multimodal, and general reasoning. Qwen 3.5 keeps its coding crown. Llama 4 is only ahead when you actually need its 400B scale — which for most teams is a liability, not an asset.

Sizes & hardware

Model	Params	Min VRAM (Q4)	Runs on
Gemma 4 E2B	2B (MoE, 0.5B active)	3 GB	iPhone 16 Pro, any M-series Mac
Gemma 4 E4B	4B (MoE, 1B active)	5 GB	16GB M1/M2/M3 Mac, RTX 4060
Gemma 4 26B	26B (MoE, 4B active)	16 GB	RTX 4090, M4 Pro 48GB
Gemma 4 31B Dense	31B	20 GB	RTX 4090, M3 Max 64GB
Qwen 3.5 7B	7B	5 GB	Most consumer GPUs
Qwen 3.5 35B	35B	22 GB	RTX 4090, M-series 64GB
Qwen 3.5 Coder 32B	32B	20 GB	RTX 4090
Llama 4 70B	70B	42 GB	2× RTX 4090, H100
Llama 4 400B	400B (MoE, 60B active)	250 GB	4× H100 80GB or cluster

Gemma 4’s MoE architecture is the headline: the 26B-A4B runs at the cost of a 4B model but performs near-frontier. That is why it sits #6 on the full Arena leaderboard — beating several closed models.

1. Gemma 4 — Best quality per parameter

Released April 2, 2026 under Apache 2.0. Key facts:

Four sizes: E2B, E4B, 26B-A4B MoE, 31B Dense
Native multimodal: text, vision, and audio in; text out
128K context on E2B/E4B, 256K on 26B/31B
Apache 2.0 — fully open, no MAU cap
Official GGUF, MLX, and vLLM support at launch

Strengths: Best-in-class quality per active parameter, true Apache 2.0, native multimodal, excellent on-device sizes, strong Arena rankings.

Weaknesses: Short context on small sizes (128K vs Qwen’s 1M on some variants), coding still a step behind Qwen 3.5 Coder, MoE inference can be tricky to optimize on older GPUs.

Best for: Local assistants, edge deployment, multimodal RAG, anyone who wants the best open model they can run on a single GPU.

2. Qwen 3.5 — Best open model for coding

Alibaba’s Qwen 3.5 family (shipped early 2026) remains the strongest open coding line:

Qwen 3.5 Coder 32B still #1 on open-source coding leaderboards
Small variants (1.5B, 7B) offer the best long-context recall in their size class
Apache 2.0 for the base models
Strong multilingual coverage (Chinese, Arabic, Japanese)

Strengths: Best open-source coding model, excellent long-context, strong multilingual, huge Chinese community + tooling.

Weaknesses: Multimodal still weaker than Gemma 4, Qwen 3.5 VL trails Gemma 4 on MMMU, some variants have China-aligned safety tuning that may not match Western use cases.

Best for: Coding workloads, multilingual apps, long-document RAG, Chinese-language deployments.

3. Llama 4 — Best when you need scale

Meta’s Llama 4 remains the largest generally available open model:

70B and 400B MoE sizes
10M-token context (largest in the open world)
Llama Community License (700M MAU cap)
Strong agent performance in larger sizes

Strengths: Largest open model available, best long-context for entire-codebase ingestion, mature ecosystem (Ollama, vLLM, TGI all first-class).

Weaknesses: Not Apache / MIT — license is a deal-breaker for some startups, harder to self-host (needs multi-GPU cluster for 400B), Gemma 4 matches or beats 70B while being smaller, Muse Spark is now Meta’s real flagship.

Best for: Enterprise deployments that can run 400B, research groups needing 10M context, any org already on Meta’s ecosystem.

Head-to-head: run an Astro blog coding assistant locally

We ran the same 20 issue-implementation tasks on a Mac Studio M4 Max 128GB:

Metric	Gemma 4 31B	Qwen 3.5 Coder 32B	Llama 4 70B
Tasks passing tests	14 / 20	17 / 20	12 / 20
Tokens / sec	48	52	24
Memory peak	22 GB	21 GB	44 GB
Ease of setup (Ollama)	1 command	1 command	1 command

Qwen 3.5 Coder won code quality. Gemma 4 31B was close and noticeably better at reasoning about the codebase structure. Llama 4 70B felt over-qualified — slower, bigger, and not meaningfully better for this use case.

Quick decision guide

If your priority is…	Choose
Best open model on one GPU	Gemma 4 31B
Best open model on a Mac	Gemma 4 26B MoE
Smallest useful model (mobile)	Gemma 4 E2B
Open-source coding	Qwen 3.5 Coder 32B
Longest context	Llama 4 (10M tokens)
Multilingual	Qwen 3.5
True Apache 2.0	Gemma 4 or Qwen 3.5
Apple Silicon (MLX)	Gemma 4 (first-class MLX)

Verdict

Gemma 4 is the new default open model for April 2026. It ships under a real Apache 2.0 license, runs on consumer hardware, matches or beats everything in its size class, and is natively multimodal out of the box. If you are starting a new self-hosted stack, start with Gemma 4.

Qwen 3.5 Coder is the exception. For pure coding, it is still the best open model and probably will be until Qwen 4 lands.

Llama 4 is becoming a specialty tool. Unless you actually need 10M context or 400B scale, smaller Gemma / Qwen variants deliver better quality on better hardware with better licenses. And with Meta’s own attention moving to Muse Spark, don’t expect Llama 5 to arrive soon.