Gemma 4 Models: Which One Should You Actually Use?

Gemma 4 Model Sizes Explained

The four Gemma 4 variants target different hardware and use cases. Understanding the naming convention saves a lot of confusion. "E" stands for effective parameters (what actually runs during inference on edge devices), while the 26B model uses "A" for active parameters in its MoE architecture. Here's what each one does.

According to HuggingFace's Gemma 4 analysis, the MoE architecture in the 26B model differs from DeepSeek and Qwen. Instead of replacing MLP blocks with sparse experts, Gemma adds MoE blocks as separate layers alongside the standard MLP blocks and sums their outputs. This design choice keeps the base model intact while adding specialized capacity through experts.

Gemma 4 vs Llama 4 vs Mistral Small 4

Three major open model families dropped updates in early 2026. Each targets different strengths. I've been running all three locally and here's how they compare in practice.

Gemma 4 wins on parameter efficiency. No other family offers models as small as E2B that still produce usable output. If you're building for mobile or embedded devices, Gemma is your only serious option among the big three. The MoE architecture also means the 26B model runs like a 4B model in terms of speed and memory.

Llama 4 wins on context length. The 10 million token context window on Llama 4 Scout is in a different league. If you're processing entire codebases, long documents, or need to maintain very long conversations, Llama is the clear choice. But you need server hardware to run it - there's no laptop-friendly Llama 4 variant.

Mistral Small 4 wins on coding quality per parameter. In my testing, Mistral produces the cleanest code output when you compare similar-sized models. The 24B dense architecture means every parameter contributes to every token, and it shows in structured output tasks. According to BenchLM's 2026 rankings, Mistral consistently scores well on code generation benchmarks relative to its size.

Gemma 4 Tool Use and Agentic Workflows

The feature that excites me most about Gemma 4 is native structured tool use. All four variants can accept a function schema and return valid JSON matching that schema. No prompt engineering hacks, no output parsing tricks. You define a tool, and the model calls it correctly.

This matters because it makes local AI agents practical. I've been building MCP servers for various integrations, and Gemma 4's tool use works well enough to drive simple agentic workflows without sending data to a cloud API. Here's what a basic tool definition looks like when calling Gemma 4 through Ollama.

import requests
import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g. 'Pune' or 'San Francisco'"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = requests.post(
    "http://localhost:11434/api/chat",
    json={
        "model": "gemma4:26b",
        "messages": [
            {"role": "user", "content": "What's the weather in Pune?"}
        ],
        "tools": tools,
        "stream": False
    }
)

result = response.json()
print(json.dumps(result["message"]["tool_calls"], indent=2))

The model returns a properly structured tool call with the location extracted from the user's message. This is the same pattern that powers MCP server integrations - you define tools, the model decides when to call them, and your code handles execution. Running this locally with Gemma 4 means sensitive data never leaves your machine.

I've tested tool use across all four variants. The 26B MoE and 31B Dense handle multi-tool scenarios reliably - they pick the right tool and format parameters correctly. The E4B works for single-tool cases but sometimes struggles with complex schemas that have optional nested fields. The E2B is too small for reliable tool use in my experience.