Google just quietly dropped one of the best arguments for switching your agent stack away from proprietary APIs. On April 2, 2026, Gemma 4 landed with four variants, an Apache 2.0 license, and benchmark numbers that should genuinely unsettle the teams at DeepSeek and Meta.
The headline: Gemma 4's 31B dense model is currently ranked #3 on the open model global leaderboard (Arena AI ELO ~1452). It beats DeepSeek V3.2 Thinking — at a fraction of the parameter count. For developers building agents, it also earned a perfect score on Tool Call 15, the standard agentic function-calling benchmark.
The Four Models: What You're Actually Choosing Between
Gemma 4 ships as four distinct variants:
| Variant | Architecture | Active Params | Context | Best For |
|---|---|---|---|---|
| E2B | Dense | 2B | 128K | Mobile, edge, audio input |
| E4B | Dense | 4B | 128K | Mobile, edge, audio input |
| 26B-A4B | MoE (26B total, 4B active) | 4B | 256K | Local inference, agents |
| 31B | Dense | 31B | 256K | Maximum capability |
The 26B MoE model runs at 34 tokens/second on an M4 Mac Mini with 16GB RAM and 162 tokens/second on an RTX 4090.
Why Apache 2.0 Is The Actual Story
Everyone's debating benchmarks. The license is the story.
Previous Gemma releases shipped under Google's custom model terms. Apache 2.0 means commercial use, modification, redistribution — no strings attached. Google finally stopped trying to have it both ways.
"Gemma 4 is FINALLY Apache 2.0 aka real-open-source-licensed." — @ClementDelangue, Hugging Face CEO
Agent Benchmarks: The Numbers That Matter
Tool Call 15: Perfect score (15/15)
AIME 2026 math: 20.8% → 89.2% (post-thinking)
LiveCodeBench: 29.1% → 80.0% (post-thinking)
Context window: 256K tokens (26B/31B variants)
The Tool Call 15 perfect score is significant. This benchmark tests function call accuracy, parameter formatting, and multi-step tool chaining — exactly what production agents require.
Video: Matthew Berman's Full Breakdown
Local Deployment: Day-Zero Ecosystem
Within 24 hours of release:
- NVIDIA published NVFP4-quantized 31B — 4x smaller at frontier-level performance
- MLX support was available day-zero
- llama.cpp 2.7x faster on RTX GPUs
- Hugging Face Inference Endpoints supporting one-click deployment
# Start the 26B MoE model locally
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M
How to Download Gemma 4 Locally
Gemma 4 vs. The Competition
vs. DeepSeek V3.2: Gemma 4 31B edges it out on Arena AI at a smaller parameter count. Better licensing clarity. DeepSeek has edge cases with export controls some enterprise teams prefer to avoid.
vs. Qwen 3.5: Qwen 3.5 has strong coding benchmarks but the flagship 3.6-Plus went hosted-only. Gemma 4 is fully open and locally deployable.
vs. Llama 4: Similar tier, Llama 4 has broader ecosystem, Gemma 4 has cleaner tool calling benchmarks. Benchmark both for your use case.
vs. GPT-4o/Claude Sonnet 4: Frontier APIs still lead on very complex reasoning and 1M+ context. For most production agent workloads — RAG, function calling, structured output — Gemma 4 is competitive enough that local inference economics win.
What's Missing
- Context window: 256K is large but not frontier-large (no 1M+)
- Audio input only in edge models (E2B/E4B), not 26B/31B
- Relatively new — give the community a few weeks for production hardening
Deployment Checklist
- Match variant to hardware: 26B MoE for 16-24GB RAM; 31B for 32GB+
- Use NVIDIA NVFP4 quantization for RTX GPU deployments
- Use llama.cpp GGUF Q4_K_M for CPU/Apple Silicon
- Test Tool Call 15 with your actual tool schemas before deploying
- Start with 26B MoE for cost/quality tradeoff
Gemma 4 is the most significant open model release from Google since the Gemini architecture pivot. The combination of Apache 2.0 licensing, perfect tool call benchmark scores, 256K context, and day-zero ecosystem support makes it the strongest single argument for running open weights in production agent stacks as of April 2026.
Full article with additional detail at AgentConn
Gemma 4 is available on Hugging Face, Kaggle, and Google AI Studio.
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