Gemma 4 vs Gemma 3 comparison: should you upgrade in 2026?
If your actual question is "Gemma 4 vs Gemma 3, what changed and should I upgrade?", the direct answer is yes. Gemma 4 is not a routine refresh. On coding, math, long context, and agent-style work, it moves into a different capability class, which is why this comparison is really about deciding how quickly to switch rather than whether the upgrade exists at all.
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Benchmark comparison: Gemma 4 vs Gemma 3
These are scores from the same benchmark versions, evaluated under equivalent conditions. The Gemma 3 numbers are from its March 2025 release; the Gemma 4 numbers are from the April 2026 launch.
| Benchmark | Gemma 3 27B | Gemma 4 31B | Gemma 4 26B A4B | Change |
|---|---|---|---|---|
| AIME 2026 (math reasoning) | 20.8% | 89.2% | 88.3% | +68 points |
| LiveCodeBench v6 (coding) | 29.1% | 80.0% | 77.1% | +51 points |
| GPQA Diamond (science) | ~42% | 84.3% | 82.3% | +42 points |
| MMLU-Pro (knowledge) | ~67% | 85.2% | 82.6% | +18 points |
| BigBench Extra Hard | 19.3% | 74.4% | — | +55 points |
| Codeforces ELO | 110 | 2,150 | — | +2,040 ELO |
| LMArena (open model rank) | Not competitive | #3 globally | #6 globally | — |
The Codeforces number is the most striking: Gemma 3 27B had an ELO of 110, which means it could barely solve beginner competitive programming problems. Gemma 4 31B reaches ELO 2,150 — expert competitive programmer level. That's not an improvement in degree, it's a different capability class entirely.
The coding leap is real
LiveCodeBench went from 29.1% to 80.0%. But the more informative signal is the Codeforces ELO, because Codeforces problems have a known difficulty ceiling.
At ELO 110, Gemma 3 was below the bottom percentile of competitive programmers — it essentially couldn't solve problems designed to be hard. At ELO 2,150, Gemma 4 is in the top few hundred globally. That jump happened primarily because of two things Google added to Gemma 4:
- Built-in thinking mode: Gemma 4 can generate 4,000+ tokens of internal reasoning before giving a final answer, similar to DeepSeek-R1 or Claude's extended thinking. Gemma 3 had no equivalent.
- Native function calling: trained into the model from the ground up, not bolted on via prompt engineering. This makes multi-step coding tasks — run build, read error, fix code, run again — significantly more reliable.
Context window: usable long context is new
Gemma 3 had a 128K context window on paper. In practice, it struggled to actually use information from long contexts. On RULER (a test of long-context information retrieval), Gemma 3 scored 13.5% at 128K tokens — meaning it was essentially failing to use most of the context it nominally supported.
Gemma 4 scores 66.4% on the same test at 128K tokens. The larger models extend to 256K context with functional long-range recall.
This matters for real workflows: feeding an entire codebase, a long document, or an extended conversation into context and having the model actually use it reliably.
What's genuinely new in Gemma 4 that Gemma 3 didn't have
| Feature | Gemma 3 | Gemma 4 |
|---|---|---|
| Thinking / reasoning mode | ❌ Not available | ✅ Built-in, up to 4,000 reasoning tokens |
| Native function calling | Limited, prompt-engineered | ✅ Trained natively, multi-turn agentic |
| Audio input | ❌ Text and image only | ✅ E2B and E4B support speech recognition |
| Variable-resolution images | Fixed resolution only | ✅ Variable aspect ratio, configurable token budget |
| MoE architecture option | ❌ Dense models only | ✅ 26B A4B: 26B total, only 4B active |
| Context window (large models) | 128K (poorly utilized) | ✅ 256K (functionally usable) |
| License | Custom Gemma license (restrictions) | ✅ Apache 2.0 — fully commercial, no limits |
| Android Studio integration | Not officially supported | ✅ Official recommended local model |
Should you upgrade from Gemma 3?
Short answer: yes, if you're using Gemma 3 for anything beyond trivial tasks.
The only reason to stay on Gemma 3 is if:
- You have a working fine-tune of Gemma 3 and don't want to redo the fine-tuning work yet
- Your hardware genuinely can't run Gemma 4 (unlikely — Gemma 4 E4B needs similar resources to Gemma 3 4B)
- A specific tool or framework doesn't support Gemma 4 yet (day-one support was broad, but niche integrations may lag)
For general local use — coding assistance, document Q&A, agents, chat — Gemma 4 is a straightforward upgrade with no practical downside.
Gemma 3 to Gemma 4 model size mapping
Gemma 4 reorganized the size lineup, so the mapping isn't perfectly 1:1.
| If you were running | Start with | Why |
|---|---|---|
| Gemma 3 1B or 4B | Gemma 4 E4B | Similar hardware footprint, significantly better quality |
| Gemma 3 12B | Gemma 4 E4B or 26B A4B | E4B is faster; 26B A4B gives better reasoning if you have the memory |
| Gemma 3 27B | Gemma 4 26B A4B or 31B | 26B A4B uses less memory than Gemma 3 27B while outperforming it significantly |
The 26B A4B is the most interesting upgrade path from Gemma 3 27B: it fits in less VRAM (because of the MoE architecture), runs faster per token, and beats Gemma 3 27B on every benchmark. If you were running Gemma 3 27B locally, switching to 26B A4B at Q5 will feel like an upgrade in both speed and quality.