TokenMix Research Lab · 2026-04-22
Gemma 4 Review: Google's 31B Open Model Beats 600B Rivals (2026)
Google released Gemma 4 in April 2026 — four model sizes (E2B, E4B, 26B MoE, 31B Dense) under permissive Apache 2.0 license, free for commercial use. The 31B Dense variant outperforms models 20× its size on several reasoning benchmarks. The 26B MoE runs locally on 18GB RAM — meaning it fits a single consumer RTX 4090 or even a MacBook M4 Pro. But a sharp tradeoff: on pure SWE-Bench Pro, Gemma 4 still lags behind Chinese open models like GLM-5.1. This review covers what Gemma 4 actually wins, where it loses, and how it compares to the open-source top 4 in 2026. TokenMix.ai hosts all four Gemma 4 sizes at transparent per-token pricing for teams without self-hosting capacity.
Table of Contents
- Confirmed vs Speculation: Gemma 4 Claims
- The Four Gemma 4 Variants Explained
- Benchmark Reality: Where Gemma 4 Wins and Loses
- 18GB RAM Local Deployment: What Actually Works
- Gemma 4 vs Llama 4 vs GLM-5.1 vs DeepSeek V3.2
- Apache 2.0 vs Llama License: Why It Matters for Startups
- Who Should Use Gemma 4
- FAQ
Confirmed vs Speculation: Gemma 4 Claims
| Claim | Status | Source |
|---|---|---|
| Gemma 4 released April 2026 | Confirmed | Google blog |
| Four sizes: E2B, E4B, 26B MoE, 31B Dense | Confirmed | Google model card |
| Apache 2.0 license | Confirmed | Hugging Face repo |
| 31B Dense outperforms 600B models on reasoning | Confirmed (on specific benchmarks) | Google benchmark report |
| Runs on 18GB RAM | Confirmed (26B MoE quantized) | Community testing |
| "Most capable open model" | Overstated — GLM-5.1 wins SWE-Bench Pro | Independent leaderboards |
| Competitive with Claude Opus 4.7 | No on coding, close on text-only reasoning | Third-party evals |
Bottom line: Gemma 4 is the best Apache-licensed open model as of April 22, 2026 — but not the best open model overall. License and local-run capability are its killer features.
The Four Gemma 4 Variants Explained
| Variant | Total params | Active params | Best use case | Min hardware |
|---|---|---|---|---|
| Gemma 4 E2B | 2B | 2B | Edge / mobile / embedded | 4GB RAM |
| Gemma 4 E4B | 4B | 4B | Laptop / browser LLM | 8GB RAM |
| Gemma 4 26B MoE | 26B | ~4B active | Consumer GPU local | 18GB RAM (quantized) |
| Gemma 4 31B Dense | 31B | 31B | Workstation / single H100 | 80GB VRAM (fp16) |
"Effective" naming (E2B, E4B) is Google's attempt to market small models by their effective-quality tier rather than raw parameter count — these are competitive with older 7B/13B models despite smaller parameter budgets.
Benchmark Reality: Where Gemma 4 Wins and Loses
Third-party benchmark results, April 2026:
| Benchmark | Gemma 4 31B | Llama 4 Maverick 400B | GLM-5.1 744B MoE | DeepSeek V3.2 671B | Claude Opus 4.7 |
|---|---|---|---|---|---|
| MMLU | 87% | 88% | 89% | 88% | 92% |
| GPQA Diamond | 78% | 80% | 82% | 79% | 94.2% |
| SWE-Bench Verified | 64% | 71% | 78% | 72% | 87.6% |
| SWE-Bench Pro | 48% | 52% | 70% | 60% | 54% (est) |
| HumanEval | 88% | 91% | 92% | 90% | 92% |
| MATH | 85% | 83% | 89% | 87% | 93% |
| Needle-in-haystack 128K | 95% | 92% | 93% | 94% | N/A (200K default) |
Key observations:
- Gemma 4 31B punches above weight on MMLU and MATH (parity with 400B Llama 4)
- Loses on coding — GLM-5.1 is clearly ahead
- Not in Claude's league on complex reasoning (GPQA, MATH)
- Best-in-class for its size tier — dominates any open model under 50B
Reality check: when Google says "outperforms models 20x its size," they're cherry-picking specific benchmarks. On the composite average across 16 benchmarks, Gemma 4 31B Dense sits slightly below GLM-5.1 and DeepSeek V3.2, which are 20-25× larger in total parameters but only 2-3× larger in active parameters (MoE).
18GB RAM Local Deployment: What Actually Works
The "runs on 18GB RAM" claim is specific to Gemma 4 26B MoE quantized to Q4_K_M:
# Via Ollama (easiest path)
ollama pull gemma-4:26b-q4
ollama run gemma-4:26b-q4
Hardware tested (community reports):
- MacBook M4 Pro 24GB unified memory: works at ~18 tokens/sec
- RTX 4090 24GB: works at ~35 tokens/sec (fp8)
- RTX 3090 24GB: works at ~22 tokens/sec (Q4_K_M)
- Dual RTX 3060 12GB (via vLLM tensor parallel): works at ~15 tokens/sec
What doesn't work: 31B Dense on 24GB VRAM (needs 48-80GB for fp16 inference), full 128K context on any consumer hardware (KV cache blows the VRAM budget past 32K).
For production deployment beyond a single workstation, TokenMix.ai hosts Gemma 4 31B Dense at $0.25/