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# Gemma vs Qwen: Best Small Open Model in 2026
- URL: https://www.edgewisely.com/gemma-vs-qwen/
- Published: 2026-10-30T17:11:50.000Z
- Updated: 2026-10-30T17:11:50.000Z
- Description: Gemma vs Qwen compared for local and on-device use — sizes, hardware, Apache-2.0 vs Gemma terms, verified benchmarks, and a clear verdict on which to run.
- Author: John Karpentar
- Tags: AI, Engineering

**Gemma vs Qwen** comes down to one trade-off: **Qwen3** wins for coding, reasoning, and permissive licensing, while **Gemma 3** wins for vision, audio, multilingual breadth, and the smallest phone-class footprints. Ship code or a commercial product, reach for **Qwen**. Need images, sound, or a sub-2GB on-device model, reach for **Gemma**.

## TL;DR

- **Verdict:** For most developers running small open models locally, **Qwen3 is the stronger default** — better coding and reasoning scores and a clean Apache-2.0 license. **Gemma 3** is the better pick when you need built-in vision, audio, or the smallest device footprint.
- **Sizes and hardware:** Both ship a full ladder. **Qwen3** dense runs 0.6B / 1.7B / 4B / 8B / 14B / 32B; **Gemma 3** runs 1B / 4B / 12B / 27B, plus **Gemma 3n** (E2B/E4B) built for phones from **2GB of RAM**. A **27B–32B** model needs roughly a **24GB** GPU at 4-bit; an **8B** fits an **8GB** card.
- **License:** **Qwen3 is Apache-2.0** — commercial use, no strings. **Gemma uses Google's custom Gemma Terms of Use**, which carry a Prohibited Use Policy and redistribution conditions. This is the single biggest practical difference.
- **Benchmarks:** On Google's and Alibaba's own numbers, **Qwen3 32B** leads **Gemma 3 27B** on reasoning (**GPQA Diamond 54.6 vs 42.4**) and coding (**LiveCodeBench 31.3 vs 29.7**, and far higher with Qwen's thinking mode).
- **Multimodality:** **Gemma 3** (4B and up) and **Gemma 3n** are natively multimodal — image, and for 3n also **audio and video**. Plain **Qwen3** dense models are text-only; vision lives in a separate **Qwen3-VL** line.

## Gemma vs Qwen: which is better?

There is no single winner — the honest answer is "which better for what."

**Qwen3** is the stronger generalist for text work. It posts higher reasoning and coding scores at matched sizes, ships a hybrid thinking/non-thinking mode, and carries the friction-free **Apache-2.0** license. If your job is code, agents, math, or anything you want to sell, Qwen is the safer default.

**Gemma 3** wins on three specific fronts: **native vision** baked into the 4B/12B/27B models, **multilingual coverage** (**140+ languages**, per [Google's Gemma 3 release notes](https://huggingface.co/blog/gemma3?ref=edgewisely.com)), and **true on-device reach** through the mobile-first Gemma 3n line.

So "gemma vs qwen, which is better" depends entirely on whether you need pictures and phones (Gemma) or code and licensing freedom (Qwen).

### The real current versions (ignore the spam)

Before the numbers, a warning. The web is full of fabricated version names — "Gemma 4," "Qwen 3.5/3.6/3.8," mystery "27B" variants. We checked primary sources, and **as of October 2026 the real current open generations are Gemma 3 and Qwen3**. Treat any "Gemma 4" or "Qwen3.8" spec sheet as unverified SEO filler until Google or Alibaba publishes it on an official channel.

## The spec comparison

Here are the matched tiers you'll actually choose between for local use. The **Runs on** column is practical 4-bit guidance, not a vendor spec; every license and context figure is maker-stated, verified October 2026.

| Model (size)  | Maker   | License     | Context | Modalities    | Runs on (4-bit)            | Best for                         |
| ------------- | ------- | ----------- | ------- | ------------- | -------------------------- | -------------------------------- |
| Gemma 3 1B    | Google  | Gemma Terms | 32K     | Text          | Phone / CPU, <2GB          | Lightweight drafting             |
| Qwen3 1.7B    | Alibaba | Apache-2.0  | 32K     | Text          | Phone / CPU                | Light text + tool calls          |
| Gemma 3 4B    | Google  | Gemma Terms | 128K    | Text + vision | 8GB GPU / laptop           | On-device vision, multilingual   |
| Qwen3 4B      | Alibaba | Apache-2.0  | 32K     | Text          | 8GB GPU / laptop           | On-device reasoning, coding      |
| Gemma 3 12B   | Google  | Gemma Terms | 128K    | Text + vision | 12GB GPU                   | Vision + chat on one card        |
| Qwen3 14B     | Alibaba | Apache-2.0  | 128K    | Text          | 12–16GB GPU                | Reasoning, coding                |
| Gemma 3 27B   | Google  | Gemma Terms | 128K    | Text + vision | 24GB GPU                   | Best Gemma vision + chat         |
| Qwen3 32B     | Alibaba | Apache-2.0  | 128K    | Text          | 24GB GPU                   | Best dense Qwen reasoning/coding |
| Qwen3-30B-A3B | Alibaba | Apache-2.0  | 128K    | Text          | 24GB GPU (3B active, fast) | Fast local MoE                   |

Two things stand out. **Gemma's 4B and up include vision**; the equivalent plain Qwen3 dense models are **text-only**. And **Qwen3-30B-A3B** is a mixture-of-experts model — it loads 30B of weights but activates only **3B per token**, so it runs far faster than a 32B dense model on the same card.

## Gemma vs Qwen for coding

For coding, **Qwen is the clear pick.**

On Alibaba's reported figures, **Qwen3 32B** scores **LiveCodeBench 31.3** in standard mode and **65.7 with thinking enabled**, plus a **CodeForces rating of 1977** in thinking mode, per [DataLearner's Qwen3-32B benchmark page](https://www.datalearner.com/en/ai-models/pretrained-models/Qwen3-32B/analysis?ref=edgewisely.com). **Gemma 3 27B** lands at **LiveCodeBench 29.7** on [Google's own Gemma 3 technical report](https://arxiv.org/html/2503.19786?ref=edgewisely.com) — close in standard mode, well behind once Qwen reasons.

Qwen also ships a dedicated coder. **Qwen3-Coder-30B-A3B** — **30.5B total, 3.3B active, a 262K context window, Apache-2.0**, per its [model profile](https://www.gradually.ai/ki-modelle/alibaba-qwen3-coder-30b-a3b-instruct/?ref=edgewisely.com) — is built for agentic coding and long repositories, and runs on a single 24GB GPU thanks to the MoE design. Gemma 3 has no coding-specialized variant.

If "gemma vs qwen for coding" is your whole question, run **Qwen3-Coder-30B-A3B** or **Qwen3 14B/32B** and don't look back.

## Gemma vs Qwen for local / on-device use

This is where **Gemma earns its keep**.

Google built **Gemma 3n** specifically for phones and laptops. The **E2B** variant runs in about **2GB of memory** and **E4B** in **3GB**, and both are natively multimodal — text, image, **audio, and video** — per [Google's Gemma 3n announcement](https://huggingface.co/blog/gemma3n?ref=edgewisely.com). Nothing in the plain Qwen3 line is engineered that aggressively for the smallest devices or ships audio input.

For a **laptop or single consumer GPU**, both families are excellent and the choice flips to your task. An **8GB** card comfortably runs **Gemma 3 4B** or **Qwen3 4B**; a **12GB** card handles **Gemma 3 12B** or **Qwen3 14B**; a **24GB** card (RTX 3090/4090 class) runs **Gemma 3 27B**, **Qwen3 32B**, or the fast **Qwen3-30B-A3B**.

For the **best open model for local** general text and coding, Qwen3 wins. For local **vision or audio** on modest hardware, Gemma wins. Whichever you choose, you'll likely serve it through [Ollama or llama.cpp](https://www.edgewisely.com/ollama-vs-llama-cpp/) — both families are first-class citizens there.

## How do the licenses compare?

This is the difference that outlives any benchmark.

**Qwen3 is Apache-2.0.** The [official Qwen3 release](https://qwenlm.github.io/blog/qwen3/?ref=edgewisely.com) states the dense and MoE models are open-weighted under Apache-2.0 — you can use, modify, redistribute, and build commercial products with no usage restrictions beyond standard attribution.

**Gemma is not open-source in that sense.** It ships under the custom **Gemma Terms of Use**, which grant broad rights but bind you to a **Prohibited Use Policy** and require that you pass the use restrictions downstream, notify recipients, and mark modified files, per the [Gemma Terms of Use](https://ai.google.dev/gemma/terms?ref=edgewisely.com). Google also reserves the right to update the models and to restrict uses it believes violate the agreement.

For a hobby project, the difference is academic. For a company shipping a product, **Apache-2.0 removes a legal review that the Gemma terms require.** If licensing certainty matters, Qwen's terms are simpler. This is the same open-weight calculus we walk through in our [DeepSeek vs Qwen comparison](https://www.edgewisely.com/deepseek-vs-qwen/).

## Gemma vs Qwen on benchmarks

Match the sizes and **Qwen3 leads on reasoning and coding**, on each vendor's own evaluation.

| Benchmark (as of Oct 2026) | Gemma 3 27B | Qwen3 32B (standard) | Qwen3 32B (thinking) |
| -------------------------- | ----------- | -------------------- | -------------------- |
| GPQA Diamond               | 42.4        | 54.6                 | 68.4                 |
| LiveCodeBench              | 29.7        | 31.3                 | 65.7                 |
| MMLU-Pro                   | 67.5        | —                    | —                    |
| MMMU (vision)              | 64.9        | —                    | —                    |

![Grouped bar chart comparing Gemma 3 27B and Qwen3 32B (standard and thinking modes) on GPQA Diamond and LiveCodeBench](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/10/gemma-vs-qwen-chart.png)

Chart: Edgewisely, data from [Google tech report + Alibaba (via DataLearner)](https://arxiv.org/html/2503.19786?ref=edgewisely.com) (as of Oct 2026)

Gemma 3 27B's **MMLU-Pro 67.5** and **MMMU 64.9** come from [Google's technical report](https://arxiv.org/html/2503.19786?ref=edgewisely.com); the Qwen3 32B figures are Alibaba's. **These are self-reported by each maker and may use different settings — read them as directional, not head-to-head.**

For an independent read, Artificial Analysis's Intelligence Index placed **Qwen3 32B above Gemma 3 27B** on its leaderboard. Note that **Artificial Analysis relaunched the index as v5.0 around 27 October 2026 with a lower re-baseline**, so older absolute scores are no longer comparable — use it for ranking, not for raw numbers. On "qwen vs gemma benchmark" questions, the ranking has been consistent: **Qwen ahead on text reasoning and code, Gemma competitive on vision and multilingual.**

Where Gemma holds ground is **vision**: its **MMMU 64.9** is a real score on a hard multimodal benchmark, and every Gemma 3 model from 4B up can read an image out of the box. To match that in the Qwen world you step outside plain Qwen3 to the **Qwen3-VL** line (2B/4B/8B/32B dense plus MoE), which is a separate download.

## Which should you run?

Pick by the job, not the brand.

- **On-device / edge, smallest footprint:** **Gemma 3n E2B/E4B** (2–3GB, audio + vision) — nothing in plain Qwen3 goes this small with this much multimodality.
- **Local coding:** **Qwen3-Coder-30B-A3B** or **Qwen3 14B/32B** — higher code scores, long context, Apache-2.0.
- **Multilingual chat:** **Gemma 3** (140+ languages) edges it, with Qwen3 a strong second.
- **Vision on a laptop:** **Gemma 3 4B/12B** for built-in simplicity, or **Qwen3-VL** if you want Qwen's reasoning with images.
- **Commercial product, licensing certainty:** **Qwen3** (Apache-2.0).
- **Fine-tuning base:** either, but Apache-2.0 makes **Qwen3** the cleaner foundation to redistribute.

Gemma also competes head-to-head with Meta's models; if that's your real comparison, see our [Qwen vs Llama breakdown](https://www.edgewisely.com/qwen-vs-llama/).

## Our take

Here's our thesis: **for the developer running small open models on their own hardware in late 2026, Qwen3 is the better default, and it isn't close for text work — but Gemma 3 is the one model family you should keep installed alongside it, because it does two things Qwen's base models simply don't.**

Start with what benchmarks miss, because for local use they miss a lot.

**Quantization quality matters more than the headline score.** A model is only as good as its 4-bit GGUF, and here both families are mature — Gemma 3 and Qwen3 both have well-behaved community quants that hold up at Q4\_K\_M. In our experience Qwen3's smaller sizes (4B, 8B) degrade more gracefully under aggressive quantization than most rivals, which is exactly what you want on an 8GB card. That's a practitioner observation, not a lab result, so weight it accordingly.

**Tokens per second is the number you'll actually feel.** This is where the **Qwen3-30B-A3B** MoE design is quietly brilliant: because only **3B parameters activate per token**, it generates far faster than a 32B dense model while keeping most of the quality, and it still fits a 24GB card. If you're choosing one local model for responsiveness on a single GPU, that MoE is our pick over any dense 27B–32B option — Gemma included. Gemma 3 has no MoE in the small range, which is a real gap for latency-sensitive local use.

**Ecosystem and tooling are a wash, which is itself notable.** Both families are day-one supported in Ollama, llama.cpp, and vLLM; both have abundant fine-tunes and LoRAs. Five years ago the "open model" conversation was dominated by Llama's tooling lead. That moat is gone — you can serve either of these as easily as any model, which is why the decision comes down to capability and license rather than plumbing.

**Now the license, because it's the part people under-weight.** Apache-2.0 versus the Gemma Terms of Use is not a formality when you ship to customers. Qwen3's Apache-2.0 means your legal team signs off in an afternoon. The Gemma terms are permissive but conditional: a Prohibited Use Policy you must propagate downstream, redistribution notices, and Google's reserved right to restrict use. For internal tools, fine. For a product you sell or a model you redistribute, **Apache-2.0 is a feature**, and it tilts us toward Qwen for anything commercial. We'd call this the single most defensible reason to default to Qwen.

**But Gemma's multimodality is a genuine, hard-to-replace advantage.** Gemma 3n reading **audio and video** on a phone in 2–3GB is not something plain Qwen3 does, and Gemma 3's vision being native in the 4B model — not a separate VL download — lowers the integration cost for image tasks. If your app is multimodal and small, Gemma is the shorter path.

So, the verdict by job:

- **Best for coding, agents, commercial products, fine-tuning base:** **Qwen3** — scores, speed (via MoE), and Apache-2.0.
- **Best for on-device multimodal, audio, smallest footprint, widest languages:** **Gemma 3 / 3n.**
- **Avoid Gemma if** your use is commercial and you want zero license friction, or your workload is pure code.
- **Avoid plain Qwen3 if** you need built-in vision or audio on a tiny device and don't want to manage a second VL model.

Our one-line recommendation: **install Qwen3 as your workhorse (8B for a laptop, 14B/32B or Qwen3-30B-A3B for a 24GB GPU), and keep Gemma 3n or Gemma 3 4B around for the multimodal and multilingual jobs Qwen's base models can't touch.** That combination covers nearly every local use case, costs nothing, and sidesteps the version-number spam entirely.

## Frequently Asked Questions

### Is Gemma better than Qwen?

Not overall. On matched sizes, **Qwen3 scores higher on reasoning and coding** and ships the simpler Apache-2.0 license, making it the better general-purpose local model. **Gemma 3 is better** for native vision, audio (via Gemma 3n), multilingual coverage, and the smallest on-device footprints. Choose by task, not by a single ranking.

### Which is better for coding, Gemma or Qwen?

**Qwen.** Qwen3 32B beats Gemma 3 27B on LiveCodeBench even in standard mode and pulls far ahead with thinking enabled. Alibaba also ships **Qwen3-Coder-30B-A3B**, a coding-specialized model with a 262K-token context that runs on a single 24GB GPU. Gemma 3 has no equivalent coding-focused variant.

### Can Gemma and Qwen run locally on a laptop?

Yes. Both offer small sizes for consumer hardware. **Qwen3 4B** or **Gemma 3 4B** run on an 8GB GPU or a modern laptop, and **Gemma 3n E2B** runs in about **2GB of RAM**, even on phones. Larger 27B–32B models need roughly a 24GB GPU at 4-bit quantization. Serve them through Ollama or llama.cpp.

### Is Qwen Apache-2.0 licensed?

Yes. Alibaba releases the Qwen3 dense and MoE open-weight models, and Qwen3-Coder, under **Apache-2.0**, which permits commercial use, modification, and redistribution. Gemma, by contrast, uses Google's **custom Gemma Terms of Use** with a Prohibited Use Policy and downstream conditions — permissive, but not the same as a standard open-source license.

### Which is best for fine-tuning?

Either fine-tunes well and both are supported across major toolchains, but **Qwen3's Apache-2.0 license makes it the cleaner base** if you plan to redistribute your fine-tuned weights. Gemma can be fine-tuned too, but you must carry the Gemma Terms and Prohibited Use Policy downstream, which adds compliance overhead for a model you intend to share or sell.

## Editor's note — sources

All model versions, sizes, context windows, licenses, and benchmark figures were verified against primary sources in October 2026\. We excluded widely circulated but unverified names ("Gemma 4," "Qwen3.8/3.6") that appear only in SEO content, not on official channels — one Google documentation URL even served an injected "deprecated, go to gemma.ai" banner, which we disregarded.

- Gemma 3 sizes, context, multimodality, 140+ languages — [Google / Hugging Face, Gemma 3 release](https://huggingface.co/blog/gemma3?ref=edgewisely.com)
- Gemma 3 benchmarks (MMLU-Pro, LiveCodeBench, GPQA, MMMU) — [Gemma 3 Technical Report (arXiv)](https://arxiv.org/html/2503.19786?ref=edgewisely.com)
- Gemma 3n E2B/E4B, memory, audio/video — [Google, Gemma 3n announcement](https://huggingface.co/blog/gemma3n?ref=edgewisely.com)
- Gemma custom license terms — [Gemma Terms of Use](https://ai.google.dev/gemma/terms?ref=edgewisely.com)
- Qwen3 lineup, Apache-2.0, context windows — [Qwen3 official release](https://qwenlm.github.io/blog/qwen3/?ref=edgewisely.com)
- Qwen3 32B benchmark scores — [DataLearner, Qwen3-32B analysis](https://www.datalearner.com/en/ai-models/pretrained-models/Qwen3-32B/analysis?ref=edgewisely.com)
- Qwen3-Coder-30B-A3B specs and license — [Qwen3-Coder-30B-A3B profile](https://www.gradually.ai/ki-modelle/alibaba-qwen3-coder-30b-a3b-instruct/?ref=edgewisely.com)
- Independent ranking — [Artificial Analysis, Qwen3 32B vs Gemma 3 27B](https://artificialanalysis.ai/models/comparisons/qwen3-32b-instruct-vs-gemma-3-27b?ref=edgewisely.com)