Gemma 4 vs Gemma 3: What Changed and Should You Switch?

If you are running Gemma 3 in production or local development, Gemma 4 is worth the migration — but it is not a drop-in swap. Gemma 4 vs Gemma 3 is a new model family with a different naming system, native multimodal inputs, an updated chat template, and a configurable thinking mode that all require changes to your code. This guide covers what changed, the benchmark numbers, hardware requirements, and what to update before you swap models.

Gemma 4 vs Gemma 3: The Headline Differences

Google released Gemma 4 in April 2026 under the Apache 2.0 license — a significant change from the Gemma Terms of Use that governed earlier models. It ships in four variants, replaces the 1B/4B/12B/27B naming convention, and adds multimodality across the entire family.

The New Model Family Names: E2B, E4B, 26B A4B, 31B

The naming convention is new and non-obvious. E2B and E4B are "effective" parameter models — they use Per-Layer Embeddings (PLE), a technique introduced in Gemma 3n, to maximize parameter efficiency for on-device deployment on phones and laptops. The "E" signals that the compute footprint is smaller than the raw parameter count suggests.

26B A4B is a Mixture-of-Experts model where "A4B" means "4 billion active parameters" — only 4B of the 26B parameters are activated during each inference pass. The 31B is a standard dense model, architecturally similar to Gemma 3 27B but with multimodal support and updated training. For a full breakdown of which variant fits which use case, see Gemma 4 vs Gemma 3 vs Gemma 3n: Which Model Makes the Most Sense in 2026?

Architecture Changes Under the Hood

For the dense 31B model, analysis of the model configs confirms the architecture is largely unchanged from Gemma 3 — slightly fewer layers (60 vs 62) but a wider model. The benchmark gains come primarily from the training recipe and data quality, not a fundamental redesign. However, several targeted changes matter for practitioners.

Multimodal by Default

All Gemma 4 models accept image input. The E2B and E4B edge models additionally accept audio — speech recognition and understanding are built in. Gemma 3 was text-only across the entire family. If you built pipelines assuming Gemma was a pure text model, multimodal inputs are a new capability, not just an upsell.

MoE for the 26B Variant

The 26B A4B is Gemma 4's first Mixture-of-Experts variant. With only 4B parameters active per token, it delivers near-30B quality while running on hardware sized for a much smaller model. The attention mechanism uses alternating local sliding-window and global full-context attention layers, with dual RoPE configurations (standard RoPE for sliding layers, proportional RoPE for global layers) and a shared KV cache across the last N layers to reduce VRAM pressure during long-context inference.

Extended Context and Thinking Mode

Edge models maintain Gemma 3's 128K context window. The larger models (26B A4B, 31B) expand to 256K tokens — enough to pass an entire repository in a single prompt. Gemma 4 also adds a configurable thinking mode: the model generates extended internal reasoning before producing a final answer, driving the large gains on math and reasoning benchmarks.

Benchmark Comparison: Gemma 4 vs Gemma 3

The performance gap between Gemma 4 and Gemma 3 is not incremental. The 31B model currently ranks #3 on the Arena AI text leaderboard; the 26B A4B ranks #6 — both positions that Gemma 3 did not hold.

⚠ Gemma 3 27B HumanEval score is estimated from Gemma 2 baseline data — verify against the official Gemma 3 technical report for your use case before making production decisions.

The coding gains matter most for this audience. A ~16-point HumanEval improvement means Gemma 4 can handle more complex completions, refactors, and test generation. For teams using Gemma 3 specifically for code tasks, this is the clearest reason to upgrade.

Hardware Requirements and Performance-Per-Dollar

VRAM by Model Size

For step-by-step local deployment across platforms, see Run Gemma 4 on Your PC and Devices Locally.

The 26B A4B Efficiency Case

The most compelling hardware story is the 26B A4B. Gemma 3 27B required approximately 20 GB of VRAM at 4-bit. Gemma 4's 26B A4B needs only 18 GB at 4-bit — while activating the equivalent of a 4B model per token — and consistently outperforms the older 27B across all reported benchmarks. Same GPU, less VRAM, better results.

If you are currently running Gemma 3 12B because the 27B did not fit your GPU, test the 26B A4B — it may fit on hardware that previously could not run the largest Gemma 3 variant, and it will outperform both.

Migration Guide: What Your Code Needs to Change

Two things require code changes before you can swap models.

Chat Template Changes

Gemma 3 used a proprietary chat format with <start_of_turn> tokens. Gemma 4 uses the standard system / user / assistant role structure, aligned with the OpenAI-style chat template convention. If you hard-coded the Gemma 3 format, update it:

# Gemma 3 — old format (do not use with Gemma 4)
prompt = (
    "<start_of_turn>user
"
    "Explain RoPE positional encoding"
    "<end_of_turn>
"
    "<start_of_turn>model
"
)

# Gemma 4 — new format using HuggingFace apply_chat_template
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user",   "content": "Explain RoPE positional encoding"},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

If you use Ollama, LM Studio, or another inference tool that handles templates automatically, this is managed for you — pull the new model and you are done.

Thinking Mode and Token Budgets

Gemma 4 adds a configurable thinking mode. When enabled, the model generates extended internal reasoning before answering — up to 4,000+ tokens. This drives the AIME and GPQA gains but increases latency. You control it via the generation config:

generation_config = {
    "thinking": {
        "type": "enabled",
        "budget_tokens": 2048,  # cap reasoning tokens; 0 = off
    },
    "max_output_tokens": 1024,
}

Set budget_tokens: 0 to disable thinking for latency-sensitive applications. If you do not configure this, thinking defaults to enabled — which will produce slower responses than Gemma 3 if you are not expecting it.

Should You Switch?

• Switch immediately if you need multimodal input — Gemma 3 cannot process images or audio.
• Switch immediately if you are on Gemma 3 27B and want better benchmarks for the same or lower VRAM — the 26B A4B delivers this.
• Switch if you need longer context — 256K on larger models vs 128K on Gemma 3.
• Switch for Apache 2.0 compliance — if your legal team has concerns about the Gemma Terms of Use, the license change resolves them.
• Stay on Gemma 3 if your pipeline is tightly coupled to the old chat template and you have no bandwidth to update it, or if your current benchmarks already meet requirements and you cannot absorb migration risk.
• Stay on Gemma 3 if you need a sub-2B model — Gemma 4 E2B is the smallest variant and may not fit on the most constrained devices where Gemma 3 1B ran.

For most practitioners running Gemma 3 in a standard inference pipeline, the migration is bounded work: update the chat template, add a thinking mode flag, and pull the new model. Against the benchmark gains — especially the 26B A4B efficiency story — the upgrade is worth it.

If you are evaluating Gemma against other open-source options, the Gemma 3 vs Qwen 3 in-depth comparison remains a useful reference for the broader competitive landscape.