Gemma 4: Benchmark Results & Transparency Report
Reproducible, standardized metrics across reasoning, coding, multilingual, and efficiency domains
Methodology & Transparency: All benchmark results are evaluated under controlled, reproducible conditions using industry-standard datasets. Scores represent average performance across 5 independent runs with fixed temperature (0.1) and top-p (0.95) sampling. Hardware configurations, quantization levels, and evaluation scripts are publicly available for independent verification.
Core Reasoning & Knowledge
📚 MMLU-Pro
Advanced multi-domain knowledge & complex reasoning across 30+ academic disciplines
🧮 GSM8K
Grade-school math word problems requiring multi-step arithmetic & logical deduction
🔬 GPQA Diamond
Graduate-level science & expert-verified question answering in physics, biology, chemistry
🎯 ARC-Challenge
Scientific reasoning & commonsense knowledge applied to complex multiple-choice scenarios
Code Generation & Engineering
💻 HumanEval (0-shot)
Synthetic Python function completion with strict pass@1 evaluation
MBPP+
Real-world programming tasks with explicit test cases & edge-case coverage
🐛 SWE-bench Verified
Autonomous bug fixing & feature implementation in real GitHub repositories
📊 DS-1000
Data science & visualization code generation across pandas, matplotlib, seaborn
Multilingual & Cross-Cultural Evaluation
🌍 FLORES-200
Cross-lingual translation accuracy across 200+ languages & dialects
️ MGSM
Multilingual grade-school math reasoning in 10+ high & low-resource languages
🔄 XCOPA
Cross-lingual commonsense causal reasoning with cultural context alignment
Performance in languages with limited high-quality training corpora (e.g., certain indigenous or regional dialects) shows higher variance. Domain-specific fine-tuning is recommended for production deployment in these contexts.
Efficiency & Deployment Metrics
- Token Generation Speed: 142 tok/s (BF16, RTX 4090) | 89 tok/s (8-bit) | 67 tok/s (4-bit)
- Memory Footprint: 18GB VRAM (BF16) | 10GB VRAM (8-bit) | 5.2GB VRAM (4-bit) for 27B variant
- First-Token Latency: 180ms average across quantization levels on consumer hardware
- Quantization Overhead: <2.8% average capability loss at 4-bit precision across all major benchmarks
- Batch Throughput: Scales linearly up to batch size 32 with optimized vLLM/TensorRT-LLM engines
Comparative Leaderboard Positioning
Gemma 4 27B
Outperforms previous-generation 70B+ open models in reasoning & coding while using 60% less VRAM. Competitive with leading proprietary models in multilingual & efficiency metrics.
Generation-over-Generation Gains
+14.2% MMLU-Pro, +8.7% HumanEval, +11.3% FLORES-200, and -22% latency compared to Gemma 3 under identical hardware conditions.
All evaluation scripts, hardware configurations, and raw output logs are published alongside this report. We encourage independent researchers to verify, fork, and extend these benchmarks. Results may vary slightly based on environment, driver versions, and prompt templating.
Download Data & Evaluation Tools
Access raw metrics, interactive charts, and reproducible evaluation pipelines:
⚠️ Benchmark Disclaimer
Metrics represent controlled, synthetic evaluations under optimal conditions. Real-world performance depends on prompt engineering, domain specificity, quantization settings, and deployment architecture. Benchmarks measure capability ceilings, not guaranteed production outcomes. Always validate against your specific workload before scaling.