Gemma 3 27B

Gemma 3 marks a decisive leap forward in open vision-language models. The 27B instruction-tuned variant (Gemma-3-27B-IT) delivers a 1338 Elo score on LMSys Chatbot Arena, ranking in the top 10 across all models and outperforming most open-weight peers—including DeepSeek-V3, Qwen2.5-72B, and even Meta’s 405B LLaMA 3.1. On standard benchmarks, it posts strong results: 67.5 on MMLU-Pro, 29.7 on LiveCodeBench, and 54.4 on Bird-SQL, showing robust reasoning and coding ability. Its 89.0 on MATH and 74.9 on FACTS Grounding reflect precision in symbolic tasks and factual alignment. This is enabled by a novel post-training pipeline blending distillation, RLHF (with reward models like BOND and WARP), and extensive multilingual tuning across 140+ languages using a 262K-token SentencePiece tokenizer. Architecturally, Gemma 3 introduces efficient long-context handling (up to 128K tokens) through RoPE scaling and 5:1 local-to-global attention layering—cutting KV cache memory by up to 85% vs. global-only designs without hurting perplexity. Multimodal inputs are powered by a frozen 400M SigLIP vision encoder and enhanced at inference with Pan & Scan, helping Gemma 3 excel on real-world image tasks (e.g., +17 points on InfoVQA with P&S). Its release spans 1B to 27B dense models with instruction-tuned and pre-trained variants—all deployable via Hugging Face, MLX, or llama.cpp. With day-zero support across tooling, near-SOTA performance, and strong safety benchmarks, Gemma 3 is a high-performing, accessible alternative to Gemini 1.5-Pro and a defining model in the open frontier.