Gemma 3 4B vs Qwen3.8 27B

Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.

The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.

Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.

Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.