TokenMix Research Lab · 2026-04-22

Gemma 4 Review: Google's 31B Open Model Beats 600B Rivals (2026)

Gemma 4 Review: Google's 31B Open Model Beats 600B Rivals (2026)

Last Updated: 2026-04-22
Author: TokenMix Research Lab

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

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:

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):

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/$1.00 per million tokens — cheaper than running your own 8× A100 setup below ~200M tokens/month.

Gemma 4 vs Llama 4 vs GLM-5.1 vs DeepSeek V3.2

The 2026 open-source top 4 ranked by use case:

Use case Best choice Why
Laptop / local / private Gemma 4 26B MoE Runs on 18GB, Apache license, good quality
Coding agent (enterprise) GLM-5.1 SWE-Bench Pro SOTA, MIT license
Cost-optimized SaaS DeepSeek V3.2 $0.14/$0.28 per MTok hosted
Long context (10M+) Llama 4 Maverick 10M context window, strong NIH recall
Reasoning / math Gemma 4 31B Dense Best size-efficiency for math/science
Truly open (modify + redistribute) Gemma 4 (Apache) or GLM-5.1 (MIT) Both are permissive

Gemma 4's edge is local-run quality. If you need a model that fits on a dev's MacBook for private workloads, Gemma 4 26B MoE has no real competitor.

Apache 2.0 vs Llama License: Why It Matters for Startups

Three common open licenses, ranked by permissiveness:

License Modify Redistribute Commercial use Restrictions
MIT (GLM-5.1) Yes Yes Yes None
Apache 2.0 (Gemma 4, Qwen3) Yes Yes Yes Patent grant, attribution
Llama Community (Llama 4) Yes Yes Yes 700M+ user cap, output training restriction
DeepSeek License Yes Yes Yes Use-case restrictions

For startups, Apache 2.0 vs Llama Community License is a meaningful difference:

If your product uses synthetic data generation for training (fine-tunes, RAG, agent evaluation), Gemma 4's Apache license is the safer choice. Consult legal on your specific flow.

Who Should Use Gemma 4

Profile Use Gemma 4? Which size?
Individual dev, local testing Yes 26B MoE on Ollama
On-device mobile AI Yes E2B or E4B
Enterprise self-hosted LLM Yes 31B Dense on single H100
Production API (small scale) Yes 31B via TokenMix.ai hosted
Production API (large scale, coding) No — use GLM-5.1 —
Privacy-sensitive on-prem Yes 31B Dense
Real-time chat latency Yes E4B for sub-100ms

FAQ

Is Gemma 4 better than Llama 4?

Depends on what you measure. On reasoning (MMLU, GPQA, MATH), Gemma 4 31B is competitive with Llama 4 Maverick 400B despite being 13× smaller. On long context (Llama 4: 10M vs Gemma 4: 128K), Llama wins. On license permissiveness, Gemma 4's Apache 2.0 beats Llama's Community License.

Can I run Gemma 4 on a MacBook?

Yes, the 26B MoE variant runs on any Mac with 18GB+ unified memory. Recommended: M4 Pro or better with 24GB+ for comfortable 18 tokens/sec. E4B runs on any M-series Mac at 40+ tokens/sec.

Is Gemma 4 good for coding?

Mediocre. SWE-Bench Verified ~64%, HumanEval ~88%. Strong for small tasks but behind GLM-5.1 (78% Verified) and Claude Opus 4.7 (87.6%) for serious coding agents. For coding, prefer GLM-5.1 or Claude.

What's the catch with Apache 2.0 license?

Nothing significant for most users. Apache 2.0 requires attribution (include the license with any redistribution) and includes a patent grant (Google can't sue you for patent infringement on the model). No MAU caps, no output restrictions.

How does Gemma 4 compare to Claude Opus 4.7?

Different leagues. Claude Opus 4.7 is the paid frontier flagship with 92% MMLU, 94.2% GPQA, 87.6% SWE-bench Verified — priced at $5/$25 per million tokens. Gemma 4 is the best open-source model in its size tier, free to run locally. Use Claude for paid quality ceiling, Gemma 4 for private/local workloads. See our Claude Opus 4.7 review for the full spec comparison.

Does Gemma 4 support function calling / tool use?

Yes, 31B Dense supports structured function calling. 26B MoE has limited support. E2B and E4B do not reliably support tool use. For agentic workflows requiring tools, use 31B Dense or switch to GLM-5.1 / Claude.

Can I fine-tune Gemma 4?

Yes, Apache 2.0 license allows fine-tuning and redistribution of fine-tuned weights. Tools: LoRA via HuggingFace PEFT, full fine-tune via TRL on 8× H100 for 31B Dense. Community fine-tunes (medical, legal, coding-specific) are expected within 60 days of release.

Sources

By TokenMix Research Lab · Updated 2026-04-22