Gemma 3 vs Llama 3.3: I Tested Both — Here's the Winner

I threw the same 12 prompts at Gemma 3 27B and Llama 3.3 70B expecting a clear winner.

Didn’t get one. What I got was way more interesting.

These two models are the most popular free options on OpenRouter, and they take completely different approaches. Gemma is Google’s compact powerhouse — 27 billion parameters, trained as a distilled version of Gemini. Llama is Meta’s open-source giant at 70 billion parameters, nearly 3x the size.

Bigger should mean better, right? Not always.

The Setup

I tested both models using our AI comparator tool with identical prompts across three categories:

Writing — blog intros, email rewrites, creative descriptions
Coding — Python functions, debugging, code review
Reasoning — logic puzzles, analysis, multi-step problems

Each model got the exact same prompt, same settings, no system message tweaks. Raw performance, head to head.

Writing: Gemma Surprised Me

Here’s what happened when I asked both models to write an intro paragraph for a tech blog post about remote work tools:

Gemma 3 27B came back with something I could almost publish as-is. Natural tone, varied sentence length, a hook that actually hooked. It even threw in a slightly sarcastic aside that felt genuinely human. Took 3.2 seconds.

Llama 3.3 70B produced a technically solid paragraph. Well-structured, hit all the key points. But it read like… well, like an AI wrote it. Clean, correct, a little sterile. 4.8 seconds.

I ran four more writing prompts. Same pattern every time — Gemma consistently produced text with more personality and flow. Llama’s output was reliable but bland.

Writing winner: Gemma 3. And it wasn’t close.

Coding: Llama Takes Over

Different story with code.

I asked both to write a Python function that takes a nested JSON object and flattens it into dot-notation keys. Standard interview-style problem.

Llama nailed it. Clean solution, handled edge cases I didn’t even mention (empty arrays, null values, nested lists), added type hints, and the docstring actually explained what the function does. Not what it “might” do — what it does.

Gemma’s version worked but missed the nested list case. It also used a recursive approach that would blow the stack on deeply nested objects. Functional, sure. Production-ready? No.

Three more coding prompts. Llama won every single one. The 70B parameter advantage really shows when you need precise, structured output.

Coding winner: Llama 3.3. Decisively.

Reasoning: Closer Than Expected

This is where it got interesting.

Logic puzzle: “A farmer has a fox, a chicken, and a bag of grain. He needs to cross a river in a boat that can only carry one item at a time…”

Both solved it correctly. Both laid out the steps clearly. Llama was slightly more structured in its explanation; Gemma was slightly more concise. Call it a tie.

Math word problem involving percentages and compound interest:

Llama got the right answer. Gemma got the right answer too, but showed its work more clearly — almost like a teacher explaining to a student rather than just solving.

After six reasoning prompts, my scoreboard showed Llama winning 4, Gemma winning 2. But Gemma’s losses were narrow — it got the concept right but occasionally fumbled on the final calculation step.

Reasoning winner: Llama 3.3. But Gemma held its own.

Speed

This one matters more than people think. If you’re using AI in a workflow — writing emails, generating code, brainstorming — waiting 6 seconds versus 3 seconds adds up fast.

Task	Gemma 3 27B	Llama 3.3 70B
Short response (1-2 sentences)	~1.5s	~2.8s
Medium response (1-2 paragraphs)	~3.2s	~4.8s
Long response (full article outline)	~5.1s	~7.3s

Gemma is consistently faster. Makes sense — it’s less than half the size. That 27B vs 70B difference translates directly to inference speed.

Speed winner: Gemma 3. About 40% faster on average.

The Size Question

Gemma 3 runs on 27 billion parameters. Llama 3.3 on 70 billion. That’s a massive gap.

In practice, this means:

If you self-host, Gemma fits on a single GPU. Llama needs two or more.
Via API, the size difference doesn’t matter much (someone else handles the hardware).
For the free tier on OpenRouter, both are available at zero cost. No reason to pick based on size alone.

But parameter count isn’t everything. Gemma was trained using knowledge distillation from Google’s much larger Gemini models. Think of it as a student who learned from a genius professor — it punches above its weight class because of how it was trained, not just how big it is.

When to Use Which

After 12 head-to-head tests, here’s my honest take:

Pick Gemma 3 27B when you need:

Natural-sounding writing (blog posts, emails, creative content)
Fast responses for interactive workflows
Running locally on limited hardware
Multilingual content (supports 140+ languages)

Pick Llama 3.3 70B when you need:

Coding tasks (generation, review, debugging)
Complex reasoning or math
Following detailed, multi-step instructions
Maximum accuracy over speed

Pick both when you’re not sure. That’s literally why we built the comparator. Run your actual prompt through both models side by side and see which output you prefer for your specific use case.

The Verdict

There’s no single “better” model here. Gemma writes better. Llama codes better. Gemma is faster. Llama is more precise.

If I had to pick one for general daily use? Gemma 3 27B. The speed advantage and writing quality make it more pleasant to work with, and it’s good enough at coding and reasoning for most tasks.

But if I’m writing code or solving complex problems? Llama 3.3 70B, every time.

The good news: both models are available for free on OpenRouter. You can compare other free AI models head-to-head in our comparator right now. No signup, no API key, no credit card.

Compare free AI models in our comparator →

Tested on March 2026 using AI Prompt Race comparator. Results may vary based on prompt complexity and OpenRouter server load. Model availability may change over time.