Gemma 4 (Google)
A Tier · 8.3/10
Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices
Score Breakdown
Benchmark Scores
Benchmarks for Gemma 4 31B
| Benchmark | Description | Score | |
|---|---|---|---|
| MMLU | Knowledge across 57 subjects | 83% | |
| GPQA Diamond | Graduate-level science questions | 84.3% | |
| AIME 2026 | 89.2% | ||
| HumanEval | Python code generation | 85% |
Last updated: 2026-04-13
Personality & Tone
The compact Google cousin
Tone: Similar corporate-Google tone as Gemini but smaller and less polished. Gemma's chat replies are short, cautious, and structured -- closer to a careful intern than a peer.
Quirks: Inherits a Gemini-like safety bias, so refusals appear on prompts Mistral or DeepSeek would answer. Best used as a cheap local fallback or on-device model, not as a personality play.
The Good and the Bad
What we like
- +Apache 2.0 license -- truly permissive, you can use it commercially without strings attached
- +Multimodal: handles text + image input (audio on smaller models), generates text output
- +256K token context window -- larger than most open models
- +140+ language support -- one of the strongest multilingual open models available
- +Four sizes (E2B, E4B, 26B MoE, 31B Dense) cover edge devices to data centers
- +31B Dense scores 89% on AIME 2026 and 84% on GPQA Diamond -- competitive with frontier closed models
- +31B Dense currently ranks #3 among open models on the LMArena text leaderboard (26B MoE ranks #6) -- genuinely competitive with the top Chinese and Meta open-weight families
- +26B MoE activates only 3.8B params during inference for fast tokens-per-second
What could be better
- −Requires technical setup unless you use a hosted API provider
- −Quality still trails the very best closed models (GPT-5.4 Pro, Claude Mythos 5, Gemini 3.1 Ultra) on hardest reasoning tasks
- −No native chat UI from Google -- you're either coding against an API or using a third-party frontend
- −Smaller community than Llama -- fewer fine-tunes and tooling integrations exist
Pricing
Self-hosted
- ✓Apache 2.0 license
- ✓Free download from Hugging Face/Kaggle/Ollama
- ✓Run on your own hardware
API (OpenRouter, Gemma 4 31B)
- ✓Hosted inference
- ✓$0.14 input / $0.40 output
- ✓No infrastructure setup
Google AI Studio
- ✓Free tier for testing
- ✓Web playground access
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Gemma 4 E2B / E4B (edge-class) | 2-3 GB VRAM Q4 (runs on phones and laptops) | 8-12 GB VRAM FP16 |
| Gemma 4 26B MoE | 8 GB VRAM (Q4) | 32 GB VRAM FP16 |
| Gemma 4 31B Dense (flagship) | 12 GB VRAM Q4 (RTX 4070) | 1× A100 40 GB FP16 |
Known Issues
- 2026-04-18 BF16 stability refresh -- Google re-released Gemma 4 multimodal checkpoints in BF16 format focused on truthfulness, JSON / tool-call formatting, long-context extraction reliability, and loop resistance. Not a new model version; a quality refresh that fixes specific failure modes developers were hitting in production. If you pulled weights before 2026-04-18, consider re-downloading for the new checkpointsSource: Google DeepMind Gemma page, Hugging Face · 2026-04
- Gemma 4 launched April 2, 2026 with improved licensing -- earlier Gemma versions had restrictive use clauses that confused developersSource: The Register, Hugging Face · 2026-04
- Function calling support is new -- some users report inconsistent tool-use behavior compared to Llama 3 or MistralSource: Hugging Face discussions · 2026-04
Best for
Developers and businesses who need a permissively licensed multimodal LLM they can self-host or fine-tune. Especially good for multilingual use cases and on-device deployment.
Not for
Non-technical users who just want to chat with an AI -- there's no consumer-facing app. Use Gemini if you want a polished chat experience.
Our Verdict
Gemma 4 is Google's answer to the open-weights race against Meta's Llama and the wave of strong Chinese open models. The Apache 2.0 license is a big deal -- it removes the legal friction that made earlier Gemma adoption awkward. The 31B Dense model is genuinely competitive with frontier closed models on benchmarks while costing $0.14/M input via API. If you're building a product on open-weights LLMs and you need multimodal + multilingual + permissive licensing, Gemma 4 is now a top choice.
Sources
- Google DeepMind Gemma 4 page (accessed 2026-04-08)
- Google blog: Gemma 4 launch (accessed 2026-04-08)
- Artificial Analysis benchmarks (accessed 2026-04-08)
- OpenRouter pricing (accessed 2026-04-08)
- The Register coverage (accessed 2026-04-08)
Explore more Gemma 4 (Google) rankings
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Alternatives to Gemma 4 (Google)
Llama 4 (Meta)
Meta's open-weights family -- Scout (10M context), Maverick (multimodal 400B MoE). NOTE: Meta's frontier work moved to the proprietary Muse Spark line in April 2026; Llama remains downloadable and supported but is effectively in maintenance mode
Mistral AI
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DeepSeek
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DiffusionGemma (Google)
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MiniMax M3
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